A Platform for efficient Supply Chain Management Support in Logistics - Bevermeier, Maik; Flanke, Stephan; Haeb-Umbach, Reinhold; Stehr, Jan (International Workshop on Intelligent Transportation (WIT 2011))

Uncertainty Decoding and Conditional Bayesian Estimation - Haeb-Umbach, Reinhold (Robust Speech Recognition of Uncertain or Missing Data)
In this contribution classification rules for HMM-based speech recognition in the presence of a mismatch between training and test data are presented. The observed feature vectors are regarded as corrupted versions of underlying and unobservable clean feature vectors, which have the same statistics as the training data. Optimal classification then consists of two steps. First, the posterior density of the clean feature vector, given the observed feature vectors, has to be determined, and second, this posterior is employed in a modified classification rule, which accounts for imperfect estimates. We discuss different variants of the classification rule and further elaborate on the estimation of the clean speech feature posterior, using conditional Bayesian estimation. It is shown that this concept is fairly general and can be applied to different scenarios, such as noisy or reverberant speech recognition.

Können Computer sprechen und hören, sollen sie es überhaupt können? Sprachverarbeitung und ambiente Intelligenz - Haeb-Umbach, Reinhold (Baustelle Informationsgesellschaft und Universität heute)

Adaptive Systems for Unsupervised Speaker Tracking and Speech Recognition - Herbig, Tobias; Gerl, Franz; Minker, Wolfgang; Haeb-Umbach, Reinhold (Evolving Systems)

A Model-Based Approach to Joint Compensation of Noise and Reverberation for Speech Recognition - Krueger, Alexander; Haeb-Umbach, Reinhold (Robust Speech Recognition of Uncertain or Missing Data)
Employing automatic speech recognition systems in hands-free communication applications is accompanied by perfomance degradation due to background noise and, in particular, due to reverberation. These two kinds of distortion alter the shape of the feature vector trajectory extracted from the microphone signal and consequently lead to a discrepancy between training and testing conditions for the recognizer. In this chapter we present a feature enhancement approach aiming at the joint compensation of noise and reverberation to improve the performance by restoring the training conditions. For the enhancement we concentrate on the logarithmic mel power spectral coefficients as features, which are computed at an intermediate stage to obtain the widely used mel frequency cepstral coefficients. The proposed technique is based on a Bayesian framework, to attempt to infer the posterior distribution of the clean features given the observation of all past corrupted features. It exploits information from a priori models describing the dynamics of clean speech and noise-only feature vector trajectories as well as from an observation model relating the reverberant noisy to the clean features. The observation model relies on a simplified stochastic model of the room impulse response (RIR) between the speaker and the microphone, having only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is finally experimentally studied by means of recognition accuracy obtained for a connected digits recognition task under different noise and reverberation conditions using the Aurora~5 database.

MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations - Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011))
The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is superposed by additive Gaussian observation errors of known variance. Simulations on artificially generated data demonstrate the superiority of the proposed method. While reducing to the ordinary MAP approach in the absence of observation noise, the improvement becomes the more pronounced the larger the variance of the observation noise. The method is further extended to track the parameters in case of non-stationary Gaussian processes.
Keywords: Gaussian processes; MAP-based estimation; maximum a posteriori method; maximum likelihood estimation; nonstationary Gaussian processes

Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation - Krueger, Alexander; Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we present a novel blocking matrix and fixed beamformer design for a generalized sidelobe canceler for speech enhancement in a reverberant enclosure. They are based on a new method for estimating the acoustical transfer function ratios in the presence of stationary noise. The estimation method relies on solving a generalized eigenvalue problem in each frequency bin. An adaptive eigenvector tracking utilizing the power iteration method is employed and shown to achieve a high convergence speed. Simulation results demonstrate that the proposed beamformer leads to better noise and interference reduction and reduced speech distortions compared to other blocking matrix designs from the literature.
Keywords: acoustical transfer function ratio; adaptive eigenvector tracking; array signal processing; beamformer design; blocking matrix; eigenvalues and eigenfunctions; eigenvector-based transfer function ratios estimation; generalized sidelobe canceler; interference reduction; iterative methods; power iteration method; reduced speech distortions; reverberant enclosure; reverberation; speech enhancement; stationary noise

Conditional Bayesian Estimation Employing a Phase-Sensitive Observation Model for Noise Robust Speech Recognition - Leutnant, Volker; Haeb-Umbach, Reinhold (Robust Speech Recognition of Uncertain or Missing Data)
In this contribution, conditional Bayesian estimation employing a phase-sensitive observation model for noise robust speech recognition will be studied. After a review of speech recognition under the presence of corrupted features, termed uncertainty decoding, the estimation of the posterior distribution of the uncorrupted (clean) feature vector will be shown to be a key element of noise robust speech recognition. The estimation process will be based on three major components: an a priori model of the unobservable data, an observation model relating the unobservable data to the corrupted observation and an inference algorithm, finally allowing for a computationally tractable solution. Special stress will be laid on a detailed derivation of the phase-sensitive observation model and the required moments of the phase factor distribution. Thereby, it will not only be proven analytically that the phase factor distribution is non-Gaussian but also that all central moments can (approximately) be computed solely based on the used mel filter bank, finally rendering the moments independent of noise type and signal-to-noise ratio. The phase-sensitive observation model will then be incorporated into a model-based feature enhancement scheme and recognition experiments will be carried out on the Aurora~2 and Aurora~4 databases. The importance of incorporating phase factor information into the enhancement scheme is pointed out by all recognition results. Application of the proposed scheme under the derived uncertainty decoding framework further leads to significant improvements in both recognition tasks, eventually reaching the performance achieved with the ETSI advanced front-end.

A versatile Gaussian splitting approach to non-linear state estimation and its application to noise-robust ASR - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (Interspeech 2011)
In this work, a splitting and weighting scheme that allows for splitting a Gaussian density into a Gaussian mixture density (GMM) is extended to allow the mixture components to be arranged along arbitrary directions. The parameters of the Gaussian mixture are chosen such that the GMM and the original Gaussian still exhibit equal central moments up to an order of four. The resulting mixtures{\rq} covariances will have eigenvalues that are smaller than those of the covariance of the original distribution, which is a desirable property in the context of non-linear state estimation, since the underlying assumptions of the extended K ALMAN filter are better justified in this case. Application to speech feature enhancement in the context of noise-robust automatic speech recognition reveals the beneficial properties of the proposed approach in terms of a reduced word error rate on the Aurora 2 recognition task.

On Initial Seed Selection for Frequency Domain Blind Speech Separation - Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (Interspeech 2011)
In this paper we address the problem of initial seed selection for frequency domain iterative blind speech separation (BSS) algorithms. The derivation of the seeding algorithm is guided by the goal to select samples which are likely to be caused by source activity and not by noise and at the same time originate from different sources. The proposed algorithm has moderate computational complexity and finds better seed values than alternative schemes, as is demonstrated by experiments on the database of the SiSEC2010 challenge.

Robust Speech Recognition of Uncertain or Missing Data --- Theory and Applications

Unsupervised learning of acoustic events using dynamic time warping and hierarchical K-means++ clustering - Schmalenstroeer, Joerg; Bartek, Markus; Haeb-Umbach, Reinhold (Interspeech 2011)
In this paper we propose to jointly consider Segmental Dynamic Time Warping and distance clustering for the unsupervised learning of acoustic events. As a result, the computational complexity increases only linearly with the dababase size compared to a quadratic increase in a sequential setup, where all pairwise SDTW distances between segments are computed prior to clustering. Further, we discuss options for seed value selection for clustering and show that drawing seeds with a probability proportional to the distance from the already drawn seeds, known as K-means++ clustering, results in a significantly higher probability of finding representatives of each of the underlying classes, compared to the commonly used draws from a uniform distribution. Experiments are performed on an acoustic event classification and an isolated digit recognition task, where on the latter the final word accuracy approaches that of supervised training.

Unsupervised Geometry Calibration of Acoustic Sensor Networks Using Source Correspondences - Schmalenstroeer, Joerg; Jacob, Florian; Haeb-Umbach, Reinhold; Hennecke, Marius; Fink, Gernot A. (Interspeech 2011)
In this paper we propose a procedure for estimating the geometric configuration of an arbitrary acoustic sensor placement. It determines the position and the orientation of microphone arrays in 2D while locating a source by direction-of-arrival (DoA) estimation. Neither artificial calibration signals nor unnatural user activity are required. The problem of scale indeterminacy inherent to DoA-only observations is solved by adding time difference of arrival (TDOA) measurements. The geometry calibration method is numerically stable and delivers precise results in moderately reverberated rooms. Simulation results are confirmed by laboratory experiments.

Investigations into Features for Robust Classification into Broad Acoustic Categories - Schmalenstroeer, Joerg; Bartek, Markus; Haeb-Umbach, Reinhold (37. Deutsche Jahrestagung fuer Akustik (DAGA 2011))
In this paper we present our experimental results about classifying audio data into broad acoustic categories. The reverberated sound samples from indoor recordings are grouped into four classes, namely speech, music, acoustic events and noise. We investigated a total of 188 acoustic features and achieved for the best configuration a classification accuracy better than 98%. This was achieved by a 42-dimensional feature vector consisting of Mel-Frequency Cepstral Coefficients, an autocorrelation feature and so-called track features that measure the length of ''traces'' of high energy in the spectrogram. We also found a 4-feature configuration with a classification rate of about 90% allowing for broad acoustic category classification with low computational effort.

Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter - Bevermeier, Maik; Walter, Oliver; Peschke, Sven; Haeb-Umbach, Reinhold (7th Workshop on Positioning Navigation and Communication (WPNC 2010))
In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car.
Keywords: altitude measurement unit; barometers; barometric altimeter; barometric error Kalman filter; barometric height estimation; direct cosine matrix; global positioning system; Global Positioning System; GPS device; height information; height measurement; inertial measurement unit; Kalman filters; loosely-coupled error state space Kalman filter; loosely-coupled Kalman-filter; map matching; robust information; robust location estimation; sensor fusion; topographic map information; vertical user position

Model-Based Feature Enhancement for Reverberant Speech Recognition - Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications.
Keywords: ASR; AURORA5 database; automatic speech recognition; Bayesian inference; belief networks; CMLLR; computational complexity; constrained maximum likelihood linear regression; least mean squares methods; LMPSC computation; logarithmic Mel power spectrum; maximum likelihood estimation; Mel frequency cepstral coefficients; MFCC feature vectors; microphone signal; minimum mean square error estimation; model-based feature enhancement; regression analysis; reverberant speech recognition; reverberation; RIR energy; room impulse response; speech recognition; stochastic observation model; stochastic processes

Options for Modelling Temporal Statistical Dependencies in an Acoustic Model for ASR - Leutnant, Volker; Haeb-Umbach, Reinhold (36. Deutsche Jahrestagung fuer Akustik (DAGA 2010))
Traditionally, ASR systems are based on hidden Markov models with Gaussian mixtures modelling the state-conditioned feature distribution. The inherent assumption of conditional independence, stating that a feature's likelihood solely depends on the current HMM state, makes the search computationally tractable, nevertheless has also been identified to be a major reason for the lack of robustness of such systems. Linear dynamic models have been proposed to overcome this weakness by employing a hidden dynamic state process underlying the observed features. Though performance of linear dynamic models on continuous speech/phone recognition tasks has been shown to be superior to that of equivalent static models, this approach still cannot compete with the established acoustic models. In this paper we consider the combination of hidden Markov models based on Gaussian mixture densities (GMM-HMMs) and linear dynamic models (LDMs) as the acoustic model for automatic speech recognition systems. In doing so, the individual strengths of both models, i.e. the modelling of long-term temporal dependencies by the GMM-HMM and the direct modelling of statistical dependencies between consecutive feature vectors by the LDM, are exploited. Phone classification experiments conducted on the TIMIT database indicate the prospective use of this approach for the application to continuous speech recognition.

On the Exploitation of Hidden Markov Models and Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous Speech Recognition - Leutnant, Volker; Haeb-Umbach, Reinhold (Interspeech 2010)
Linear dynamic models (LDMs) have been shown to be a viable alternative to hidden Markov models (HMMs) on small-vocabulary recognition tasks, such as phone classification. In this paper we investigate various statistical model combination approaches for a hybrid HMM-LDM recognizer, resulting in a phone classification performance that outperforms the best individual classifier. Further, we report on continuous speech recognition experiments on the AURORA4 corpus, where the model combination is carried out on wordgraph rescoring. While the hybrid system improves the HMM system in the case of monophone HMMs, the performance of the triphone HMM model could not be improved by monophone LDMs, asking for the need to introduce context-dependency also in the LDM model inventory.

Ungrounded Independent Non-Negative Factor Analysis - Raj, Bhiksha; Wilson, Kevin W.; Krueger, Alexander; Haeb-Umbach, Reinhold (Interspeech 2010)
We describe an algorithm that performs regularized non-negative matrix factorization (NMF) to find independent components in non-negative data. Previous techniques proposed for this purpose require the data to be grounded, with support that goes down to 0 along each dimension. In our work, this requirement is eliminated. Based on it, we present a technique to find a low-dimensional decomposition of spectrograms by casting it as a problem of discovering independent non-negative components from it. The algorithm itself is implemented as regularized non-negative matrix factorization (NMF). Unlike other ICA algorithms, this algorithm computes the mixing matrix rather than an unmixing matrix. This algorithm provides a better decomposition than standard NMF when the underlying sources are independent. It makes better use of additional observation streams than previous non-negative ICA algorithms.

An EM Approach to Integrated Multichannel Speech Separation and Noise Suppression - Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (International Workshop on Acoustic Echo and Noise Control (IWAENC 2010))
In this contribution we provide a unified treatment of blind source separation (BSS) and noise suppression, two tasks which have traditionally been considered different and for which quite different techniques have been developed. Exploiting the sparseness of the sources in the short time frequency domain and using a probabilistic model which accounts for the presence of additive noise and which captures the spatial information of the multi-channel recording, a speech enhancement system is developed which suppresses noise and simultaneously separates speakers in case multiple speakers are active. Source activity estimation and model parameter estimation form the E-step and the M-step of the Expectation Maximization algorithm, respectively. Experimental results obtained on the dataset of the Signal Separation Evaluation Campaign 2010 demonstrate the effectiveness of the proposed system.

Blind speech separation employing directional statistics in an Expectation Maximization framework - Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010))
In this paper we propose to employ directional statistics in a complex vector space to approach the problem of blind speech separation in the presence of spatially correlated noise. We interpret the values of the short time Fourier transform of the microphone signals to be draws from a mixture of complex Watson distributions, a probabilistic model which naturally accounts for spatial aliasing. The parameters of the density are related to the a priori source probabilities, the power of the sources and the transfer function ratios from sources to sensors. Estimation formulas are derived for these parameters by employing the Expectation Maximization (EM) algorithm. The E-step corresponds to the estimation of the source presence probabilities for each time-frequency bin, while the M-step leads to a maximum signal-to-noise ratio (MaxSNR) beamformer in the presence of uncertainty about the source activity. Experimental results are reported for an implementation in a generalized sidelobe canceller (GSC) like spatial beamforming configuration for 3 speech sources with significant coherent noise in reverberant environments, demonstrating the usefulness of the novel modeling framework.
Keywords: array signal processing; blind source separation; blind speech separation; complex vector space; complex Watson distribution; directional statistics; expectation-maximisation algorithm; expectation maximization algorithm; Fourier transform; Fourier transforms; generalized sidelobe canceller; interference suppression; maximum signal-to-noise ratio beamformer; microphone signal; probabilistic model; spatial aliasing; spatial beamforming configuration; speech enhancement; statistical distributions

Online Diarization of Streaming Audio-Visual Data for Smart Environments - Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (IEEE Journal of Selected Topics in Signal Processing)
For an environment to be perceived as being smart, contextual information has to be gathered to adapt the system's behavior and its interface towards the user. Being a rich source of context information speech can be acquired unobtrusively by microphone arrays and then processed to extract information about the user and his environment. In this paper, a system for joint temporal segmentation, speaker localization, and identification is presented, which is supported by face identification from video data obtained from a steerable camera. Special attention is paid to latency aspects and online processing capabilities, as they are important for the application under investigation, namely ambient communication. It describes the vision of terminal-less, session-less and multi-modal telecommunication with remote partners, where the user can move freely within his home while the communication follows him. The speaker diarization serves as a context source, which has been integrated in a service-oriented middleware architecture and provided to the application to select the most appropriate I/O device and to steer the camera towards the speaker during ambient communication.
Keywords: audio streaming; audio visual data streaming; context information speech; face identification; face recognition; image segmentation; middleware; multimodal telecommunication; online diarization; service oriented middleware architecture; sessionless telecommunication; software architecture; speaker identification; speaker localization; speaker recognition; steerable camera; telecommunication computing; temporal segmentation; terminal-less telecommunication; video streaming

Robust vehicle localization based on multi-level sensor fusion and online parameter estimation - Bevermeier, Maik; Peschke, Sven; Haeb-Umbach, Reinhold (6th Workshop on Positioning Navigation and Communication (WPNC 2009))
In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding.
Keywords: covariance matrices; expectation-maximisation algorithm; expectation-maximization algorithm; global positioning system; Global Positioning System; GPS; inertial measurement unit; interacting multiple model approach; Kalman filters; multilevel sensor fusion; narrow street canyons; narrow tunnels; online parameter estimation; parameter estimation; road vehicles; robust vehicle localization; sensor fusion; state noise covariances; time-variant multilevel Kalman filter; vehicle tracking algorithm

Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning - Bevermeier, Maik; Peschke, Sven; Haeb-Umbach, Reinhold (IEEE 69th Vehicular Technology Conference (VTC 2009 Spring))
In this paper we present a novel vehicle tracking method which is based on multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman filtering of GPS and IMU measurements the estimates of the orientation of the vehicle are combined in an optimal manner to improve the robustness towards drift errors. The tracking algorithm incorporates the estimation of time-variant covariance parameters by using an iterative block Expectation-Maximization algorithm to account for time-variant driving conditions and measurement quality. The proposed system is compared to an interacting multiple model approach (IMM) and achieves improved localization accuracy at lower computational complexity. Furthermore we show how the joint parameter estimation and localizaiton can be conducted with streaming input data to be able to track vehicles in a real driving environment.
Keywords: computational complexity; expectation-maximisation algorithm; Global Positioning System; inertial measurement unit; inertial navigation; interacting multiple model; iterative block expectation-maximization algorithm; Kalman filters; multi-stage Kalman filter; parameter estimation; road vehicles; vehicle positioning; vehicle tracking

Eine Plattform fuer Mehrwertdienste im Bereich Logistik - Drahtlose Fahrzeug- und Laderaumueberwachung fuer LKW mit Hilfe einer Maut-On-Board Unit - Bevermeier, Maik; Peschke, Sven; Haeb-Umbach, Reinhold (DGON Navigationskonvent 2009)

Model based feature enhancement for automatic speech recognition in reverberant environments - Krueger, Alexander; Haeb-Umbach, Reinhold (Interspeech 2009)
In this paper we present a new feature space dereverberation technique for automatic speech recognition. We derive an expression for the dependence of the reverberant speech features in the log-mel spectral domain on the non-reverberant speech features and the room impulse response. The obtained observation model is used for a model based speech enhancement based on Kalman filtering. The performance of the proposed enhancement technique is studied on the AURORA5 database. In our currently best configuration, which includes uncertainty decoding, the number of recognition errors is approximately halved compared to the recognition of unprocessed speech.

On the Estimation and Use of Feature Reliability Information for Noise Robust Speech Recognition - Leutnant, Volker; Haeb-Umbach, Reinhold (International Conference on Acoustics (NAG/DAGA 2009))
In this paper we present an Uncertainty Decoding rule which exploits feature reliability information and interframe correlation for noise robust speech recognition. The reliability information can be obtained either from conditional Bayesian estimation, where speech and noise feature vectors are tracked jointly, or by augmenting conventional point estimation methods with heuristics about the estimator's reliability. Experimental results on the AURORA2 database demonstrate on the one hand that Uncertainty Decoding improves recognition performance, while on the other hand it is seen that the severe approximations needed to arrive at computationally tractable solutions have their noticable impact on recognition performance.

An analytic derivation of a phase-sensitive observation model for noise robust speech recognition - Leutnant, Volker; Haeb-Umbach, Reinhold (Interspeech 2009)
In this paper we present an analytic derivation of the moments of the phase factor between clean speech and noise cepstral or log-mel-spectral feature vectors. The development shows, among others, that the probability density of the phase factor is of sub-Gaussian nature and that it is independent of the noise type and the signal-to-noise ratio, however dependent on the mel filter bank index. Further we show how to compute the contribution of the phase factor to both the mean and the vari- ance of the noisy speech observation likelihood, which relates the speech and noise feature vectors to those of noisy speech. The resulting phase-sensitive observation model is then used in model-based speech feature enhancement, leading to significant improvements in word accuracy on the AURORA2 database.

A GPS positioning approach exploiting GSM velocity estimates - Peschke, Sven; Bevermeier, Maik; Haeb-Umbach, Reinhold (6th Workshop on Positioning Navigation and Communication (WPNC 2009))
A combination of GPS (global positioning system) and INS (inertial navigation system) is known to provide high precision and highly robust vehicle localization. Notably during times when the GPS signal has a poor quality, e.g. due to the lack of a sufficiently large number of visible satellites, the INS, which may consist of a gyroscope and an odometer, will lead to improved positioning accuracy. In this paper we show how velocity information obtained from GSM (global system for mobile communications) signalling, rather than from a tachometer, can be used together with a gyroscope sensor to support localization in the presence of temporarily unavailable GPS data. We propose a sensor fusion system architecture and present simulation results that show the effectiveness of this approach.
Keywords: cellular radio; distance measurement; global positioning system; Global Positioning System; global system for mobile communications; GPS positioning approach; GSM velocity; gyroscopes; gyroscope sensor; inertial navigation; inertial navigation system; odometer; sensor fusion system architecture; sensors

Verbesserung von GPS-basierter Ortung durch GSM-Geschwindigkeitsschaetzungen - Peschke, Sven; Bevermeier, Maik; Haeb-Umbach, Reinhold (DGON Navigationskonvent 2009)

Approaches to Iterative Speech Feature Enhancement and Recognition - Windmann, Stefan; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In automatic speech recognition, hidden Markov models (HMMs) are commonly used for speech decoding, while switching linear dynamic models (SLDMs) can be employed for a preceding model-based speech feature enhancement. In this paper, these model types are combined in order to obtain a novel iterative speech feature enhancement and recognition architecture. It is shown that speech feature enhancement with SLDMs can be improved by feeding back information from the HMM to the enhancement stage. Two different feedback structures are derived. In the first, the posteriors of the HMM states are used to control the model probabilities of the SLDMs, while in the second they are employed to directly influence the estimate of the speech feature distribution. Both approaches lead to improvements in recognition accuracy both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature enhancement with SLDMs. It is also shown that a combination with uncertainty decoding further enhances performance.
Keywords: AURORA2 databases; AURORA4 databases; automatic speech recognition; feedback structures; hidden Markov models; HMM; iterative methods; iterative speech feature enhancement; model probabilities; speech decoding; speech enhancement; speech feature distribution; speech recognition; switching linear dynamic models

Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition - Windmann, Stefan; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.
Keywords: AURORA4 database; blockwise EM algorithm; covariance analysis; linear state model; noise covariance; noise-robust automatic speech recognition; noisy speech cepstra; offline training mode; parameter estimation; speech recognition; speech recognition equipment; speech recognizer; state-space methods; state-space model

Audio-Visual Data Processing for Ambient Communication - Schmalenstroeer, Joerg; Leutnant, Volker; Haeb-Umbach, Reinhold (1st International Workshop on Distributed Computing in Ambient Environments within 32nd Annual Conference on Artificial Intelligence)

A hierarchical approach to unsupervised shape calibration of microphone array networks - Hennecke, Marius; Ploetz, Thomas; Fink, Gernot A.; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009))
Microphone arrays represent the basis for many challenging acoustic sensing tasks. The accuracy of techniques like beamforming directly depends on a precise knowledge of the relative positions of the sensors used. Unfortunately, for certain use cases manually measuring the geometry of an array is not feasible due to practical constraints. In this paper we present an approach to unsupervised shape calibration of microphone array networks. We developed a hierarchical procedure that first performs local shape calibration based on coherence analysis and then employs SRP-PHAT in a network calibration method. Practical experiments demonstrate the effectiveness of our approach especially for highly reverberant acoustic environments.
Keywords: acoustic sensing tasks; array geometry; calibration; coherence analysis; hierarchical procedure; local shape calibration; microphone array networks; microphone arrays; network calibration method; sensor arrays; SRP-PHAT; unsupervised shape calibration

Fusing Audio and Video Information for Online Speaker Diarization - Schmalenstroeer, Joerg; Kelling, Martin; Leutnant, Volker; Haeb-Umbach, Reinhold (Interspeech 2009)
In this paper we present a system for identifying and localizingspeakers using distant microphone arrays and a steerablepan-tilt-zoom camera. Audio and video streams are processedin real-time to obtain the diarization information grqqwho speakswhen and where'' with low latency to be used in advanced videoconferencing systems or user-adaptive interfaces. A key featureof the proposed system is to first glean information about thespeaker{\rq}s location and identity from the audio and visual datastreams separately and then to fuse these data in a probabilisticframework employing the Viterbi algorithm. Here, visual evidenceof a person is utilized through a priori state probabilities,while location and speaker change information are employedvia time-variant transition probablities. Experiments show thatvideo information yields a substantial improvement comparedto pure audio-based diarization.

Uncertainty Decoding in Automatic Speech Recognition - Haeb-Umbach, Reinhold (2008 ITG Conference on Voice Communication (SprachKommunikation))
The term uncertainty decoding has been phrased for a class of robustness enhancing algorithms in automatic speech recognition that replace point estimates and plug-in rules by posterior densities and optimal decision rules. While uncertainty can be incorporated in the model domain, in the feature domain, or even in both, we concentrate here on feature domain approaches as they tend to be computationally less demanding. We derive optimal decision rules in the presence of uncertain observations and discuss simplifications which result in computationally efficient realizations. The usefulness of the presented statistical framework is then exemplified for two types of realworld problems: The first is improving the robustness of speech recognition towards incomplete or corrupted feature vectors due to a lossy communication link between the speech capturing front end and the backend recognition engine. And the second is the well-known and extensively studied issue of improving the robustness of the recognizer towards environmental noise.

Error Concealement - Haeb-Umbach, Reinhold; Ion, Valentin (Automatic Speech Recognition on Mobile Devices and over Communication Networks)
In distributed and network speech recognition the actual recognition task is not carried out on the user{\rq}s terminal but rather on a remote server in the network. While there are good reasons for doing so, a disadvantage of this client-server architecture is clearly that the communication medium may introduce errors, which then impairs speech recognition accuracy. Even sophisticated channel coding cannot completely prevent the occurrence of residual bit errors in the case of temporarily adverse channel conditions, and in packet-oriented transmission packets of data may arrive too late for the given real-time constraints and have to be declared lost. The goal of error concealment is to reduce the detrimental effect that such errors may induce on the recipient of the transmitted speech signal by exploiting residual redundancy in the bit stream at the source coder output. In classical speech transmission a human is the recipient, and erroneous data are reconstructed so as to reduce the subjectively annoying effect of corrupted bits or lost packets. Here, however, a statistical classifier is at the receiving end, which can benefit from knowledge about the quality of the reconstruction. In this book chapter we show how the classical Bayesian decision rule needs to be modified to account for uncertain features, and illustrate how the required feature posterior density can be estimated in the case of distributed speech recognition. Some other techniques for error concealment can be related to this approach. Experimental results are given for both a small and a medium vocabulary recognition task and both for a channel exhibiting bit errors and a packet erasure channel.

A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we derive an uncertainty decoding rule for automatic speech recognition (ASR), which accounts for both corrupted observations and inter-frame correlation. The conditional independence assumption, prevalent in hidden Markov model-based ASR, is relaxed to obtain a clean speech posterior that is conditioned on the complete observed feature vector sequence. This is a more informative posterior than one conditioned only on the current observation. The novel decoding is used to obtain a transmission-error robust remote ASR system, where the speech capturing unit is connected to the decoder via an error-prone communication network. We show how the clean speech posterior can be computed for communication links being characterized by either bit errors or packet loss. Recognition results are presented for both distributed and network speech recognition, where in the latter case common voice-over-IP codecs are employed.
Keywords: automatic speech recognition; bit errors; codecs; communication links; corrupted observations; decoding; distributed speech recognition; error-prone communication network; feature vector sequence; hidden Markov model-based ASR; hidden Markov models; inter-frame correlation; Internet telephony; network speech recognition; packet loss; speech posterior; speech recognition; transmission error robust speech recognition; uncertainty decoding; voice-over-IP codecs

Investigations into Uncertainty Decoding Employing a Discrete Feature Space for Noise Robust Automatic Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (2008 ITG Conference on Voice Communication (SprachKommunikation))
This paper addresses the robustness of automatic speech recognition to environmental noise. In order to account for reliability of the clean feature estimate we employ the feature posterior density conditioned on observed noisy features to perform uncertainty decoding. We investigate two approaches to estimate the posterior using a discrete feature space, first conditioning only on the current observation, and second on the whole feature sequence of an utterance. Experiments with Aurora 2 showed that the latter provides slightly better performance, as it allows for exploiting the temporal correlations between consecutive features.

Blinde Akustische Strahlformung fuer Anwendungen im KFZ - Krueger, Alexander; Warsitz, Ernst; Haeb-Umbach, Reinhold (34. Deutsche Jahrestagung fuer Akustik (DAGA 2008))
In diesem Beitrag werden zwei neuartige akustische Strahlformungsalgorithmen fuer einen Einsatz im KFZ diskutiert. In beiden Verfahren ist fuer jede Frequenzkomponente der Eigenvektor zum groessten Eigenwert eines verallgemeinerten Eigenwertproblems zu bestimmen: bei der einen Varianate stellen die Filterkoeffizienten gerade obigen Eigenvektor dar. Sprachverzerrungen werden mit einem einkanaligen Nachfilter kompensiert. In der anderen Variante, welche die Struktur eines ''Generalized Sidelobe Cancellers'' (GSC) hat, basiert die Blockiermatrix auf obiger Eigenvektorzerlegung.

Blind Speech Separation in Presence of Correlated Noise with Generalized Eigenvector Beamforming - Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (2008 ITG Conference on Voice Communication (SprachKommunikation))
This paper considers the convolutive blind source separation of speech sources in the presence of spatially correlated noise. We introduce a method for estimating the scaled mixing matrix from the sources to the microphones even if coherent noise is present. This is achieved by combining time-frequency sparseness with the generalized eigenvalue decomposition of the power spectral density matrix (PSD) of the noisy speech and noise-only microphone signals. Separation is performed by spatial filtering with coefficients constructed by Gram-Schmidt orthogonalization which places spatial nulls at the interferer{^A}?`s direction. Experimental results show that our approach is capable of separating 2 sources in a reverberant environment (RT60=0ms..500ms) degraded by significant directional noise.

Generalized Eigenvector Blind Speech Separation Under Coherent Noise In A GSC Configuration - Tran Vu, Dang Hai; Krueger, Alexander; Haeb-Umbach, Reinhold (International Workshop on Acoustic Echo and Noise Control (IWAENC 2008))
This Paper deals with a new Technique for multi-channel separation of speech signals from convolutive mixtures under coherent noise. We demonstrate how the scaled transfer functions from the sources to the microphones can be estimated even in the presence of stationary coherent noise. The key to this are generalized eigenvalue decompositions of the power spectral density (PSD) matrices of the noisy speech and noise-only microphone signals with a controlled estimation of these matrices exploiting time-frequency sparseness of the speech sources. Separation is further improved by subsequent Gram-Schmidt orthogonalization which places spatial nulls at the interferers{\rq} directions, while noise reduction is improved by employing a novel blocking matrix and adaptive interference canceller in a Generalized Sidelobe Canceller (GSC)-like structure. We report promising experimental results for 2 speech sources with significant coherent noise in reverberant environments (RT60=0oms..500ms).

Speech enhancement with a new generalized eigenvector blocking matrix for application in a generalized sidelobe canceller - Warsitz, Ernst; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008))
The generalized sidelobe canceller by Griffith and Jim is a robust beamforming method to enhance a desired (speech) signal in the presence of stationary noise. Its performance depends to a high degree on the construction of the blocking matrix which produces noise reference signals for the subsequent adaptive interference canceller. Especially in reverberated environments the beamformer may suffer from signal leakage and reduced noise suppression. In this paper a new blocking matrix is proposed. It is based on a generalized eigenvalue problem whose solution provides an indirect estimation of the transfer functions from the source to the sensors. The quality of the new generalized eigenvector blocking matrix is studied in simulated rooms with different reverberation times and is compared to alternatives proposed in the literature.
Keywords: adaptive interference canceller; adaptive signal processing; array signal processing; beamforming method; eigenvalues and eigenfunctions; generalized eigenvector blocking matrix; generalized sidelobe canceller; interference suppression; matrix algebra; noise suppression; speech enhancement; transfer function estimation; transfer functions

Modeling the dynamics of speech and noise for speech feature enhancement in ASR - Windmann, Stefan; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008))
In this paper a switching linear dynamical model (SLDM) approach for speech feature enhancement is improved by employing more accurate models for the dynamics of speech and noise. The model of the clean speech feature trajectory is improved by augmenting the state vector to capture information derived from the delta features. Further a hidden noise state variable is introduced to obtain a more elaborated model for the noise dynamics. Approximate Bayesian inference in the SLDM is carried out by a bank of extended Kalman filters, whose outputs are combined according to the a posteriori probability of the individual state models. Experimental results on the AURORA2 database show improved recognition accuracy.
Keywords: a posteriori probability; AURORA2 database; Bayesian inference; Bayes methods; channel bank filters; extended Kalman filter banks; hidden noise state variable; Kalman filters; noise dynamics; speech enhancement; speech feature enhancement; speech feature trajectory; switching linear dynamical model approach

A novel approach to noise estimation in model-based speech feature enhancement - Windmann, Stefan; Haeb-Umbach, Reinhold (2008 ITG Conference on Voice Communication (SprachKommunikation))
In this paper, the noise estimation for model-based speech feature enhancement in automatic speech recognition (ASR) is investigated. Beside a stationary noise prior, three linear state space models for the (cepstral) noise process are considered. We have derived novel EM algorithms for the estimation of the noise model parameters: A blockwise EM algorithm is applied on noise-only input data. It is supposed to be used during the offline training mode of the recognizer. Further a sequential online EM algorithm is employed to adapt the observation variance in recognition mode which works as well under the asumption of a stationary noise prior and a linear state model for the noise. Experiments on the AURORA4 database lead to improved recognition results with the new state model compared to the assumption of stationary noise.

A segmental HMM based on a modified emission probability - Windmann, Stefan; Haeb-Umbach, Reinhold; Leutnant, Volker (2008 ITG Conference on Voice Communication (SprachKommunikation))
In this paper, a novel segmental Hidden Markov Model (HMM) is proposed. The model is based on a modified emission density where additional statistical dependencies between subsequent frames of the speech signal are considered. In the following we derive an effective search strategy for the modified statistical model. Further an approach to parameter reduction is introduced. Experiments were carried out on the AURORA2 database where consistent im

Channel Estimation by Exploiting Sublayer Information in OFDM Systems - Bevermeier, Maik; Ebel, Tobias; Haeb-Umbach, Reinhold (Multi-Carrier Spread Spectrum 2007)

Combined Time and Frequency Domain OFDM Channel Estimation - Bevermeier, Maik; Haeb-Umbach, Reinhold (Multi-Carrier Spread Spectrum 2007)

OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain - Haeb-Umbach, Reinhold; Bevermeier, Maik (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007))
In this paper we present a novel channel impulse response estimation technique for block-oriented OFDM transmission based on combining estimators: the estimates provided by a Kalman filter operating in the time domain and a Wiener filter in the frequency domain are optimally combined by taking into account their estimated error covariances. The resulting estimator turns out to be identical to the MAP estimator of correlated jointly Gaussian mean vectors. Different variants of the proposed scheme are experimentally investigated in an EEEE 802.11a-like system setup. They compare favourably with known approaches from the literature resulting in reduced mean square estimation error and bit error rate. Further, robustness and complexity issues are discussed
Keywords: bit error rate; block-oriented OFDM transmission; channel estimation; channel impulse response estimation; combining estimators; error statistics; frequency domain estimation; Gaussian mean vectors; Gaussian processes; Kalman filter; Kalman filters; MAP estimator; maximum likelihood estimation; OFDM channel estimation; OFDM modulation; time domain estimation; time-frequency analysis; Wiener filter; Wiener filters

A Novel Similarity Measure for Positioning Cellular Phones by a Comparison With a Database of Signal Power Levels - Haeb-Umbach, Reinhold; Peschke, Sven (IEEE Transactions on Vehicular Technology)
In this paper, we propose a novel similarity measure to be used for localizing mobile terminals by comparing measured signal power levels with a database of predictions. The proposed measure provides the possibility to incorporate inherent information about signal power level measurements requested by the serving base station but not reported by the mobile terminal. Increased positioning accuracy was observed both in simulations and with real field data
Keywords: cellular phone positioning; cellular radio; measured signal power levels; mobile handsets; mobility management (mobile radio)

Multi-Resolution Soft Features for Channel-Robust Distributed Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (Interspeech 2007)

Velocity Estimation of Mobile Terminals by Exploiting GSM Downlink Signalling - Peschke, Sven; Haeb-Umbach, Reinhold (4th Workshop on Positioning Navigation and Communication (WPNC 2007))
In this paper, we experimentally evaluate algorithms for velocity estimation of a GSM 900 mobile terminal which are based on the analysis of the statistical properties of the fast fading process. It is shown how theses statistics can be obtained from the training sequences present in downlink transmission bursts without establishing an active connection. Realistic simulations of a GSM channel according to the COST 207 channel models have been conducted. These models incorporate effects like multipath propagation, fading, cochannel interference and additive noise. It is shown that velocity estimation by searching for the maximum slope of the power density spectrum of the fast fading performs best.
Keywords: additive noise; cellular radio; channel estimation; cochannel interference; COST 207 channel models; downlink transmission bursts; fading channels; fading process; GSM downlink signalling; mobile terminals; multipath channels; multipath propagation; power density spectrum; statistical analysis; statistical properties; telecommunication links; telecommunication terminals; velocity estimation

Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition - Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the presence of spatially colored noise leads to a generalized eigenvalue problem. While this approach has extensively been employed in narrowband (antenna) array beamforming, it is typically not used for broadband (microphone) array beamforming due to the uncontrolled amount of speech distortion introduced by a narrowband SNR criterion. In this paper, we show how the distortion of the desired signal can be controlled by a single-channel post-filter, resulting in a performance comparable to the generalized minimum variance distortionless response beamformer, where arbitrary transfer functions relate the source and the microphones. Results are given both for directional and diffuse noise. A novel gradient ascent adaptation algorithm is presented, and its good convergence properties are experimentally revealed by comparison with alternatives from the literature. A key feature of the proposed beamformer is that it operates blindly, i.e., it neither requires knowledge about the array geometry nor an explicit estimation of the transfer functions from source to sensors or the direction-of-arrival.
Keywords: acoustic signal processing; arbitrary transfer function; array signal processing; blind acoustic beamforming; direction-of-arrival; direction-of-arrival estimation; eigenvalues and eigenfunctions; generalized eigenvalue decomposition; gradient ascent adaptation algorithm; microphone arrays; microphones; narrowband array beamforming; sensor array; single-channel post-filter; spatially colored noise; transfer functions

Blind Adaptive Principal Eigenvector Beamforming for Acoustical Source Separation - Warsitz, Ernst; Haeb-Umbach, Reinhold; Tran Vu, Dang Hai (Interspeech 2007)

An Approach to Iterative Speech Feature Enhancement and Recognition - Windmann, Stefan; Haeb-Umbach, Reinhold (Interspeech 2007)

Joint Speaker Segmentation, Localization and Identification for Streaming Audio - Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (Interspeech 2007)

Amigo Context Management Service with Applications in Ambient Communication Scenarios - Schmalenstroeer, Joerg; Leutnant, Volker; Haeb-Umbach, Reinhold (AMI-07 - European Conference on Ambient Intelligence)

Zweistufige Sprache/Pause-Detektion in stark gestoerter Umgebung - Warsitz, Ernst; Haeb-Umbach, Reinhold; Schmalenstroeer, Joerg (33. Deutsche Jahrestagung fuer Akustik (DAGA 2007))

Projekt Amigo - Sprachsignalverarbeitung im vernetzten Haus - Schmalenstroeer, Joerg; Warsitz, Ernst; Haeb-Umbach, Reinhold (33. Deutsche Jahrestagung fuer Akustik (DAGA 2007))

Comparison of Decoder-based Transmission Error Compensation Techniques for Distributed Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (7. ITG-Fachtagung Sprachkommunikation)
In this study we evaluate transmission error compensation techniques for distributed speech recognition systems based on modification of the speech decoder. The candidates are marginalization, weighted Viterbi and our recently proposed soft-feature uncertainty decoding. For the latter, it is shown how the Bayesian speech recognition approach must be reformulated for recognition at the server side. The resulting predictive classifier is able to take account of the transmission errors by changing the contribution of the affected speech features to the acoustic score. The comparison of the experimental results has proven the superiority of our approach.

An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features - Ion, Valentin; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006))
Soft-feature based speech recognition, which is an example of uncertainty decoding, has been proven to be a robust error mitigation method for distributed speech recognition over wireless channels exhibiting bit errors. In this paper we extend this concept to packet-oriented transmissions. The a posteriori probability density function of the lost feature vector, given the closest received neighbours, is computed. In the experiments, the nearest frame repetition, which is shown to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts. Taking the variance into account at the speech recognition stage results in superior performance compared to classical schemes using point estimates. A computationally and memory efficient implementation of the proposed packet loss compensation scheme based on table lookup is presented
Keywords: distributed speech recognition; least mean squares methods; MAP estimate; maximum likelihood estimation; MMSE estimate; packet loss compensation scheme; packet switched communication; posteriori probability density function; robust error mitigation method; soft-features; speech recognition; table lookup; voice communication; wireless channels

Uncertainty decoding for distributed speech recognition over error-prone networks - Ion, Valentin; Haeb-Umbach, Reinhold (Speech Communication)
In this paper, we propose an enhanced error concealment strategy at the server side of a distributed speech recognition (DSR) system, which is fully compatible with the existing DSR standard. It is based on a Bayesian approach, where the a posteriori probability density of the error-free feature vector is computed, given all received feature vectors which are possibly corrupted by transmission errors. Rather than computing a point estimate, such as the MMSE estimate, and plugging it into the Bayesian decision rule, we employ uncertainty decoding, which results in an integration over the uncertainty in the feature domain. In a typical scenario the communication between the thin client, often a mobile device, and the recognition server spreads across heterogeneous networks. Both bit errors on circuit-switched links and lost data packets on IP connections are mitigated by our approach in a unified manner. The experiments reveal improved robustness both for small- and large-vocabulary recognition tasks.
Keywords: Channel error robustness; Distributed speech recognition; Soft features; Uncertainty decoding

Improved Source Modeling and Predictive Classification for Channel Robust Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (Interspeech 2006)
The accuracy of distributed speech recognition has been shown to be very sensitive to errors occurring during transmission. One reason for this is that the classifier, usually trained under error free conditions, is unable to cope with the mismatch between an error free and error prone channel. In this paper we present a novel decision rule for classification which is able to account for channel errors. To achieve this, the classical Bayesian speech recognition approach has been reformulated for the server side, where the observation is known only to the extent, as is given by its a posteriori density function. We present a method to estimate the a posteriori density which is based on a Markov model of the source, which captures correlations of both static and dynamic features. A practical implementation is given, accompanied by experimental results for distributed speech recognition over an IP-network.

A Probabilistic Similarity Measure and a Non-Linear Post-Filter for Mobile Phone Positioning using GSM Signal Power Measurements - Peschke, Sven; Haeb-Umbach, Reinhold (European Navigation Conference & Exhibition (ENC 2006))
In this paper we present the design of a particle filter for post filtering instantaneous positioning estimates of GSM mobile terminals. The instantaneous estimates are obtained by comparing signal power levels, which are reported by the mobile terminal to the base station, with a database of predictions using a novel statistically motivated similarity measure. Unlike a simple Euclidian distance measure, the proposed scheme incorporates inherent information about signal power level measurements requested by the serving base station but not reported by the mobile terminal. Furthermore, we show how the Monte Carlo method of particle filtering helps to obtain better position estimates and, surprisingly, also helps to reduce the computational complexity. Results are presented for real field data.

Particle Filtering of Database assisted Positioning Estimates using a novel Similarity Measure for GSM Signal Power Level Measurements - Peschke, Sven; Haeb-Umbach, Reinhold (3rd Workshop on Positioning Navigation and Communication (WPNC 2006))
In this paper we present a novel and statistically motivated similarity measure for database assisted positioning of GSM mobile terminals by evaluating signal power level reports which are transmitted regulary. Unlike a simple Euclidian distance measure, the proposed scheme incorporates inherent information about signal power level measurements requested by the serving base station but not reported by the mobile terminal. Furthermore we show how the Monte Carlo method of nonlinear post filtering using particle filtering helps to obtain better position estimates and surprisingly also helps to reduce the computational complexity. Results are presented for real field data.

Mehrkanalige Sprachsignalverarbeitung durch adaptives Eigenbeamforming fuer Freisprecheinrichtungen im Kraftfahrzeug - Warsitz, Ernst; Haeb-Umbach, Reinhold (32. Deutsche Jahrestagung fuer Akustik (DAGA 2006))
Broadband adaptive beamformers, which use a narrowband SNR-maximization optimization criterion for noise reduction, typically cause distortions of the desired speech signal at the beamformer output. In this paper two methods are investigated to control the speech distortion by comparing the eigenvector beamformer with a maximum likelihood beamformer: One is an analytic solution for the ideal case of absence of reverberation and the other one is a statistically motivated approach. We use the recently introduced gradient-ascent algorithm for adaptive principal eigenvector beamforming and then normalize the filter coefficients by the proposed distortion control methods. Experimental results in terms of the achievable SNR gain and a perceptual speech quality measure are given for the normalized eigenvector beamformer and are compared to standard beamforming methods.

Controlling Speech Distortion in Adaptive Frequency-Domain Principal Eigenvector Beamforming - Warsitz, Ernst; Haeb-Umbach, Reinhold (International Workshop on Acoustic Echo and Noise Control (IWAENC 2006))
Broadband adaptive beamformers, which use a narrowband SNR-maximization optimization criterion for noise reduction, typically cause distortions of the desired speech signal at the beamformer output. In this paper two methodsare investigated to control the speech distortion by comparing the eigenvector beamformer with a maximum likelihood beamformer: One is an analytic solution for the ideal case of absence of reverberation and the other one is a statistically motivated approach. We use the recently introduced gradient-ascent algorithm for adaptive principal eigenvector beamforming and then normalize the filter coefficient s by the proposed distortion control methods. Experimental results in terms of the achievable SNR gain and a perceptual speech quality measure are given for the normalized eigenvector beamformer and are compared to standard beamforming methods.

Einkanalige Sprachsignalverbesserung mit Hilfe eines marginalisierten Partikelfilters - Windmann, Stefan; Haeb-Umbach, Reinhold (7. ITG-Fachtagung Sprachkommunikation)
Es wird ein marginalisiertes Partikelfilter beschrieben, das zur einkanaligen Sprachsignalverbesserung mit einem nichtlinearen dynamischen Zustandsmodell eingesetzt werden soll. Das System besteht aus einem Partikelfilter zum Tracking von LSP-Parametern und einem Kalman-Filter fuer jedes Partikel, das zur Sprachsignalverbesserung verwendet wird. In unserem Ansatz wird angenommen, dass die Parameter in kurzen Sprachsignalbloecken konstant sind, waehrend das Sprachsignal sich mit jedem Abtastwert aendert. Bei weissem Rauschen werden aehnliche SNR-Gewinne wie mit einem Kalman-EM-iterative Algorithmus erzielt, waehrend das Hintergrundrauschen und die Log-spektrale Distanz etwas geringer sind. Mit einem erweiterten Zustandsmodell wurden auch Untersuchungen fuer farbiges Rauschen durchgefuehrt.

Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters - Windmann, Stefan; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006))
A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved
Keywords: clean speech training data; iterative methods; iterative speech enhancement; Kalman filter; Kalman filters; Kalman-LM-iterative algorithm; line spectral pair parameters; log-spectral distance; marginalized particle filter; noise level; nonlinear dynamic state speech model; particle filtering (numerical methods); single channel speech enhancement; SNR gains; speech enhancement; speech samples

Online Speaker Change Detection by Combining BIC with Microphone Array Beamforming - Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (Interspeech 2006)
In this paper we consider the problem of detecting speaker changes in audio signals recorded by distant microphones. It is shown that the possibility to exploit the spatial separation of speakers more than makes up the degradation in detection accuracy due to the increased source-to-sensor distance compared to close-talking microphones. Speaker direction information is derived from the filter coefficients of an adaptive Filter-and-Sum Beamformer and is combined with BIC analysis. The experimental results reveal significant improvements compared to BIC-only change detection, be it with the distant or close-talking microphone.

Adaptive Filter-and-Sum Beamforming in Spatially Correlated Noise - Haeb-Umbach, Reinhold; Warsitz, Ernst (International Workshop on Acoustic Echo and Noise Control (IWAENC 2005))
In this paper we propose a novel adaptation algorithm for Filter-and-Sum beamforming in spatially correlated noise. Deterministic and stochastic gradient ascent algorithms are derived from a constrained optimization problem, which iteratively estimate the principal eigenvecto r of a generalized eigenvalue problem. The method does not require an explicit estimation of the speaker location. It is shown that the well-known Delay-and-Sum beamformer and the previously introduced Filter-and-Sum beamformer in spatially white noise are obtained as special cases. Further, bounds on the maximally achievable SNR gains are derived and it is shown that the proposed adaptation algorithm is able to approach these performance bounds.

A Unified Probabilistic Approach to Error Concealment for Distributed Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (Interspeech 2005)
The transmission errors in a wireless or packet oriented network may dramatically decrease the performance of a distributed speech recognition DSR) system. Error concealment has been shown to be an effective way to mantain an acceptable word error rate when dealing with error prone communication channels. In this paper we propose an extension of our previously introduced soft features approach for the case that the soft-output of the channel decoder is not available at the server side of the DSR system. We found a simple method to estimate bit reliability information which still gives good speech recognition results. It is shown that some other error concealment schemes turn out to be special cases of the method proposed here.

A Comparison of Soft-Feature Distributed Speech Recognition with Candidate Codecs for Speech Enabled Mobile Services - Ion, Valentin; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005))
In this paper we present a comparison of the recently proposed Soft-Feature Distributed Speech Recognition (SFDSR) with the two evaluated candidate codecs for Speech Enabled Services over wireless networks: Adaptive Multirate Codec (AMR) and the ETSI Extended Advanced Front-End for Distributed Speech Recognition (XAFE). It is shown that SFDSR achieves the best recognition performance on a simulated GSM transmission, followed by XAFE and AMR.We also present some new results concerning SFDSR which demonstrate the versatility of the approach. Further, a simple method is introduced which considerably reduces the computational effort.
Keywords: adaptive codes; adaptive multirate codec; AMR; distributed speech recognition; ETSI; extended advanced front-end; recognition performance; SFDSR; simulated GSM transmission; soft-feature distributed speech recognition; speech codecs; speech coding; speech recognition; variable rate codes; XAFE

Acoustic filter-and-sum beamforming by adaptive principal component analysis - Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005))
For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm.
Keywords: acoustic filter-and-sum beamforming; acoustic room impulses; acoustic signal processing; adaptive principal component analysis; adaptive signal processing; architectural acoustics; constrained optimization problem; cross power spectral density; deterministic algorithm; deterministic algorithms; distant-talking environments; eigenvalues and eigenfunctions; eigenvector; enhanced signal; filter-and-sum beamformer; FIR filter coefficients; FIR filter coefficients; FIR filters; gradient methods; human-machine interfaces; iterative estimation; iterative methods; largest eigenvalue; microphone signals; multichannel signal processing; optimisation; principal component analysis; spectral analysis; stochastic gradient ascent algorithm; stochastic processes

Speech Processing in the Networked Home Environment - A View on the Amigo Project - Haeb-Umbach, Reinhold; Schmalenstroeer, Joerg (Interspeech 2005)

A Comparison of Particle Filtering Variants for Speech Feature Enhancement - Haeb-Umbach, Reinhold; Schmalenstroeer, Joerg (Interspeech 2005)

Multipath-Resistant Time of Arrival Estimation for Satellite Positioning - Bischoff, R.; Haeb-Umbach, Reinhold; Nammi, Sai Ramesh (AEUe, Int. Journal on Electronics and Communications)
Satellite positioning systems, such as GPS or the future European system Galileo, employ direct-sequence spread-spectrum signals. The positioning accuracy is strongly affected by the quality of the pseudo range measurements. These measurements necessitate code and carrier synchronization of the received signal with the internally generated reference signals. In this type of systems one major error source is the multipath phenomenon, which results in a sum of delayed and weighted copies of the original signal to be present at the receiver input. This can result in a systematic error of the code tracking loop resulting in range errors in the order of several tens of meters. In this paper we propose an extension of the standard code tracking loop capable of estimating the parameters of the line-of-sight (LOS) signal and separating the LOS from the reflected signal portions. It is based on an analysis of the cross correlation of the received signal with a locally generated code sequence in the vicinity of the tracking point of a Delay-Locked Loop (DLL). For this reason, we call this method Cross Correlation Function (CCF) Analysis. The proposed method achieves considerably more accurate estimates than a DLL. Its performance is comparable to the Multipath Estimating Delay-Locked Loop (MEDLL) which is considered to be the best method for reducing multipath induced errors, so far. However, the computational complexity of the CCF Analysis is by a factor of three smaller compared to the MEDLL. Extensive simulations have been conducted for the proposed method and the MEDLL in order to assess the robustness of the two approaches under various signal constellations.

Soft Features for Improved Distributed Speech Recognition over Wireless Networks - Haeb-Umbach, Reinhold; Ion, Valentin (International Conference on Spoken Language Processing (ICSLP 2004))
A major drawback of distributed versus terminal-based speech recognition is the fact that transmission errors can lead to degraded recognition performance. In this paper we employ soft features to mitigate the effect of bit errors on wireless transmission links: At the receiver a posteriori probabilities of the transmitted feature vectors are computed by combining bit reliability information provided by the channel decoder and a priori knowledge about residual redundancy in the feature vectors. While the first-order moment of the a posteriori probability function is the MMSE estimate, the second-order moment is a measure of the uncertainty in the reconstructed features. We conducted realistic simulations of GSM transmission and achieved significant improvements in word accuracy compared to the error mitigation strategy described in the ETSI standard.

Robust speaker direction estimation with particle filtering - Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE Workshop on Multimedia Signal Processing (MMSP 2004))
The paper is concerned with binaural signal processing for a bimodal human-robot interface with hearing and vision. The two microphone signals are processed to obtain an enhanced single-channel input signal for the subsequent speech recognizer and to localize the acoustic source, an important information for establishing a natural human-robot communication. We utilize a robust adaptive algorithm for filter-and-sum beamforming (FSB) and extract speaker direction information from the resulting FIR filter coefficients. Further, particle filtering is applied which conducts a nonlinear Bayesian tracking of speaker movement. Good location accuracy can be achieved even in highly reverberant environments. The results obtained outperform the conventional generalized cross correlation (GCC) method.
Keywords: bimodal human-robot interface; binaural signal processing; enhanced single-channel input signal; filter-and-sum beamforming; filtering theory; FIR filter coefficient; generalized cross correlation method; microphones; microphone signal; nonlinear Bayesian tracking; particle filtering; robust adaptive algorithm; robust speaker direction estimation; signal processing; speech enhancement; speech recognition; speech recognizer; user interfaces

Adaptive Beamforming Combined with Particle Filtering for Acoustic Source Localization - Warsitz, Ernst; Haeb-Umbach, Reinhold; Peschke, Sven (International Conference on Spoken Language Processing (ICSLP 2004))
While the main objective of adaptive Filter-and-Sum beamforming is to obtain an enhanced speech signal for subsequent processing like speech recognition, we show how speaker localization information can be derived from the filter coefficients. To increase localization accuracy, speaker tracking is performed by non-linear Bayesian state estimation, which is realized by sequential Monte Carlo methods. Improved acquisition and tracking performance was achieved even in highly reverberant environments, in comparison with both a Kalman Filter and a recently proposed Particle Filter operating on the output of a nonadaptive Delay-and-Sum beamformer.

Auf ein Wort - Moeglichkeiten und Grenzen der automatischen Spracherkennung - Haeb-Umbach, Reinhold (Forschungsforum Paderborn)

Large Vocabulary Continuous Speech Recognition of Broadcast News - The Philips/RWTH Approach - Beyerlein, P.; Aubert, X.; Haeb-Umbach, Reinhold; Harris, M.; Klakow, D.; Wendemuth, A.; Molau, S.; Ney, N.; Pitz, Michael; Sixtus, A. (Speech Communication)
Automatic speech recognition of real-live broadcast news (BN) data (Hub-4) has become a challenging research topic in recent years. This paper summarizes our key efforts to build a large vocabulary continuous speech recognition system for the heterogenous BN task without inducing undesired complexity and computational resources. These key efforts included: - automatic segmentation of the audio signal into speech utterances; - efficient one-pass trigram decoding using look-ahead techniques; - optimal log-linear interpolation of a variety of acoustic and language models using discriminative model combination (DMC); - handling short-range and weak longer-range correlations in natural speech and language by the use of phrases and of distance-language models; - improving the acoustic modeling by a robust feature extraction, channel normalization, adaptation techniques as well as automatic script selection and verification. The starting point of the system development was the Philips 64k-NAB word-internal triphone trigram system. On the speaker-independent but microphone-dependent NAB-task (transcription of read newspaper texts) we obtained a word error rate of about 10%. Now, at the conclusion of the system development, we have arrived at Philips at an DMC-interpolated phrase-based crossword-pentaphone 4-gram system. This system transcribes BN data with an overall word error rate of about 17%.

A Joint Time Multiplex Receiver for UMTS and Galileo - Bischoff, Renke; Haeb-Umbach, Reinhold; Heinrichs, Guenther (ION-GPS 2002)
Currently the future satellite navigation system Galileo and the third generation mobile communications system UMTS are on their way to the market in Europe. cdma2000 is under development in the USA and, furthermore, a new civil GPS signal in L2 band and the new frequency band L5 are added. In a hybrid receiver for satellite navigation and mobile radio communications, the possibility of an additional usage of the mobile radio signals for navigation purposes could also be a remedy to one problem of satellite navigation systems, which is the reduced location accuracy inside of buildings and urban canyons. A hybrid receiver with two fully separated receiver branches would lead to an increased bill of material and to increased power consumption in the receiver. This paper, therefore, introduces a hybrid receiver capable of evaluating Galileo/GPS as well as UMTS/cdma2000 signals with reduced computational efforts. Furthermore, the proposed structure performs a constructive superposition of the incoming paths to improve location accuracy.

Employment of a multipath receiver structure in a combined GALILEO/UMTS receiver - Bischoff, Renke; Haeb-Umbach, Reinhold; Schulz, Wolfgang; Heinrichs, Guenther (IEEE 55th Vehicular Technology Conference (VTC 2002 Spring))
Current navigation systems like GPS (Global Positioning System) and its Russian counterpart GLONASS (Global Navigation Satellite System) only evaluate the direct signal path. The receivers treat the reflected paths also reaching the receiver antenna as disturbance which has to be suppressed. Multipath affects the tracking accuracy by resulting in a degeneration of the S-curve of the DLL (delay locked loop). Nowadays the future European systems GALILEO and GPSIIF/III with two new signals are on the way to the market and it is time to think about new receiver structures. Therefore we investigated if it is possible to use multipath for navigation constructively.
Keywords: combined GALILEO/UMTS receiver; delay locked loop; delay lock loops; DLL; Global Positioning System; GLONASS; GPS; GPSIIF/III; mobile satellite communication; multipath channels; multipath receiver structure; radio receivers; RAKE receiver; S-curve

Estimation of Bias Location Error due to Absence of the LOS-Signal in a UMTS-System - Hesse, Thomas; Bischoff, Renke; Schulz, Wolfgang; Haeb-Umbach, Reinhold (International Symposium on Location Based Services for Cellular Users (LOCELLUS 2002))
Current location methods for cellular communication systems TOA and E-OTD exploit time delays and time differences of various base station signals measured in a mobile phone to determine its location. These methods assume line-of-sight (LOS) connections to all utilized base stations. Since mobile radio channels are mainly characterized by scattered propagation paths and non-line-of-sight (NLOS) propagation, bias errors occur when measured time delays and time differences are utilized in position calculation algorithms. In this paper, the distribution of the time error due to NLOS propagation is estimated based on the channel model proposed in [1]. In combination with actual channel measurements in [2] the NLOS time error and its probability distribution function is estimated. With this information being determined for each received signal, position calculation algorithms can utilize the reliability information to enhance positioning accuracy. [1] L. J. Greenstein, V. Erceg, Y. S. Yeh, M. V. Clark, grqqA new path-gain/delay-spread propagation model for digital cellular channels'', IEEE Transactions on Vehicular Technology, Vol. 46, No. 2, May 1997 [2] H. Asplund, grqqWideband Channel Measurements in Central Stockholm'', T1P1.5/98-242r1

Implementation of a Rake Receiver Architecture into a Galileo Receiver - Bischoff, R.; Haeb-Umbach, Reinhold; Schulz, W.; Heinrichs, G. (1st ESA Workshop on Satellite Navigation User Equipment Technology (Navitec 2001))

Automatic generation of phonetic regression class trees for MLLR adaptation - Haeb-Umbach, Reinhold (IEEE Transactions on Speech and Audio Processing)
In this paper, it is shown that a correlation criterion is the appropriate criterion for bottom-up clustering to obtain broad phonetic class regression trees for maximum likelihood linear regression (MLLR)-based speaker adaptation. The correlation structure among speech units is estimated on the speaker-independent training data. In adaptation experiments the tree outperformed a regression tree obtained from clustering according to closeness in acoustic space and achieved results comparable with those of a manually designed broad phonetic class tree
Keywords: acoustic space; adaptation experiments; automatic generation; bottom-up clustering; broad phonetic class regression trees; correlation criterion; correlation methods; maximum likelihood estimation; maximum likelihood linear regression based speaker adaptation; MLLR adaptation; pattern clustering; phonetic regression class trees; speaker-independent training data; speech recognition; speech units; statistical analysis; trees (mathematics)

Multiclass linear dimension reduction by weighted pairwise Fisher criteria - Loog, M.; Duin, R.P.W.; Haeb-Umbach, Reinhold (IEEE Transactions on Pattern Analysis and Machine Intelligence)
We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidean distance of the respective class means. We generalize upon LDA by introducing a different weighting function
Keywords: approximate pairwise accuracy; Bayes error; Bayes methods; error statistics; Euclidean distance; Fisher criterion; linear dimension reduction; linear discriminant analysis; pattern classification; statistical analysis; statistical pattern classification; weighting function

Multi-class Linear Feature Extraction by Nonlinear PCA - Duin, Robert P.W.; Loog, Marco; Haeb-Umbach, Reinhold (International Conference on Pattern Recognition (ICPR 2000))
The traditional way to find a linear solution to the feature extraction problem is based on the maximization of the class-between scatter over the class-within scatter (Fisher mapping). For the multi-class problem this is, however, sub-optimal due to class conjunctions, even for the simple situation of normal distributed classes with identical covariance matrices. We propose a novel, equally fast method, based on nonlinear PCA. Although still sub-optimal, it may avoid the class conjunction. The proposed method is experimentally compared with Fisher mapping and with a neural network based approach to nonlinear PCA. It appears to outperform both methods, the first one even in a dramatic way.

Data-driven Phonetic Regression Class Tree Estimation for MLLR Adaptation - Haeb-Umbach, Reinhold (International Conference on Spoken Language Processing (ICSLP 2000))

LDA derived cepstral trajectory filters in adverse environmental conditions - Lieb, M.; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2000))
Amongst several data driven approaches for designing filters for the time sequence of spectral parameters, the linear discriminant analysis (LDA) based method has been proposed for automatic speech recognition. Here we apply LDA-based filter design to cepstral features, which better match the inherent assumption of this method that feature vector components are uncorrelated. Extensive recognition experiments have been conducted both on the standard TIMIT phone recognition task and on a proprietary 130-words command word task under various adverse environmental conditions, including reverberant data with real-life room impulse responses and data processed by acoustic echo cancellation algorithms. Significant error rate reductions have been achieved when applying the novel long-range feature filters compared to standard approaches employing cepstral mean normalization and delta and delta-delta features, in particular when facing acoustic echo cancellation scenarios and room reverberation. For example, the phone accuracy on reverberated TIMIT data could be increased from 50.7% to 56.0%
Keywords: acoustic echo cancellation algorithms; adverse environmental conditions; automatic speech recognition; cepstral analysis; cepstral features; cepstral mean normalization; command word task; delta-delta features; delta features; echo suppression; error rate reductions; feature vector components; FIR filters; LDA derived cepstral trajectory filters; linear discriminant analysis; long-range feature filters; phone accuracy; real-life room impulse responses; reverberant data; spectral parameters; speech recognition; standard TIMIT phone recognition task

Multi-class Linear Dimension Reduction by Generalized Fisher Criteria - Loog, M.; Haeb-Umbach, Reinhold (International Conference on Spoken Language Processing (ICSLP 2000))

The Philips/RWTH system for transcription of broadcast news - Beyerlein, Peter; Aubert, Xavier L.; Haeb-Umbach, Reinhold; Harris, Matthew J.; Klakow, Dietrich; Wendemuth, Andreas; Molau, Sirko; Pitz, Michael; Sixtus, Achim (Eurospeech)
This paper contains a description of the Philips/RWTH 1998 HUB4 system which has been build in a joint e ort of Philips Research Laboratories Aachen and Aachen University of Technology. We will focus our discussion on recent improvements compared to the original 1997 HUB4 system and evaluate them on the HUB4'97 evaluation data. The paper will deal with 1. a rough system overview including feature extraction, acoustic training, audio stream segmentation, and decoding 2. log-linear interpolation of distance-language models, 3. and the integration of various acoustic and language models via Discriminative Model Combination (DMC). The performance of the described system is 23% (relative) better than the performance of the 1997 Philips HUB4 system. A word error rate of 17.9% was achieved on the 1997 HUB4 evaluation set, compared to 23.5% using the original 1997 system.

The Philips/RWTH System for Transcription of Broadcast News - Beyerlein, Peter; Aubert, Xavier L.; Haeb-Umbach, Reinhold; Harris, Matthew J.; Klakow, Dietrich; Wendemuth, Andreas; Molau, Sirko; Pitz, Michael; Sixtus, Achim (Broadcast News Transcription and Understanding Workshop, Washington)
This paper contains a description of the Philips/RWTH 1998 HUB4 system which has been build in a joint e ort of Philips Research Laboratories Aachen and Aachen University of Technology. We will focus our discussion on recent improvements compared to the original 1997 HUB4 system and evaluate them on the HUB4'97 evaluation data. The paper will deal with 1. a rough system overview including feature extraction, acoustic training, audio stream segmentation, and decoding 2. log-linear interpolation of distance-language models, 3. and the integration of various acoustic and language models via Discriminative Model Combination (DMC). The performance of the described system is 23% (relative) better than the performance of the 1997 Philips HUB4 system. A word error rate of 17.9% was achieved on the 1997 HUB4 evaluation set, compared to 23.5% using the original 1997 system.

Investigations on inter-speaker variability in the feature space - Haeb-Umbach, Reinhold (ICASSP99 Phoenix, AZ)
We apply Fisher variate analysis to measure the effectiveness of speaker normalization techniques. A trace criterion, which measures the ratio of the variations due to different phonemes compared to variations due to different speakers, serves as a first assessment of a feature set without the need for recognition experiments. By using this measure and by recognition experiments we demonstrate that cepstral mean normalization also has a speaker normalization effect, in addition to the well-known channel normalization effect. Similarly vocal tract normalization (VTN) is shown to remove inter-speaker variability. For VTN we show that normalization on a per sentence basis performs better than normalization on a per speaker basis. Recognition results are given on Wall Street Journal and Hub-4 databases

An Investigation of Cepstral Parameterisations for Large Vocabulary Speech Recognition - Haeb-Umbach, Reinhold; Loog, Marco (Eurospeech)
We examined variants of MFCC and PLP cepstral parameterisations in the context of large vocabulary continuous speech recognition under different acous-tical environmental conditions: Compared to MFCC, mel-frequency PLP uses a cubic root intensity-to-loudness law, and an LPC analysis is applied to the mel-warped spectrum. In LPC-smoothed MFCC, the only difference to MFCC is the additional LPC smoothing of the warped spectrum. While neither technique was able to significantly outperform the MFCC parameterisation in our setup which includes an LDA feature transformation, feature set combination via DMC at the acoustic likelihood level and via ROVER at the recognized word level delivered small but consistent improvements.

A study of broadcast news audio stream segmentation and segment clustering - Harris, Matthew J.; Aubert, Xavier L.; Haeb-Umbach, Reinhold; Beyerlein, Peter (Eurospeech)
In transcription of broadcast news, dividing the signal into homogeneous segments, and clustering together similar segments is important. Decoding a complete broadcast news program in one chunk is technically di cult. Also, through creation of homogeneous clusters of segments, improvement from adaptation can be increased. Two systems of segmentation and clustering are compared. The best system used the BIC algorithm to produce long, homogeneous segments, and a nearest neighbour bottom-up agglomerative clustering algorithm to produce homogeneous clusters. Adaptation brought a word error rate (WER) improvement from 23:4% to 21:0% using the automatic segmentation and clustering, compared to an improvement from 21:8% to 20:0% using a handmade \correct" segmentation and clustering.

Automatic Transcription of English Broadcast News - Beyerlein, Peter; Aubert, Xavier L.; Haeb-Umbach, Reinhold; Klakow, Dietrich; Ullrich, Meinhard; Wendemuth, Andreas; Wilcox, Patricia (DARPA Broadcast News Transcription and Understanding Workshop, Landsdowne)
In this paper the Philips Broadcast News transcription system is described. The Broadcast News task aims at the recognition of "found" speech in radio and television broadcasts without any additional side information (e.g. speaking style, background conditions). The system was derived from the Philips continuous mixture density crossword HMM system, using MFCC features and Laplacian densities. A segmentation was performed to obtain sentence-like partitions of the broadcasts. Using data-driven clustering, the obtained segments were grouped into clusters with similar acoustic conditions for adaptation purposes. Gender independent word-internal and crossword triphone models were trained on 70 hours of the HUB4 training data. No focus condition specific training was applied. Channel and speaker normalization was done by mean and variance normalization as well as VTN and MLLR. The transcription was produced by an adaptive multiple pass decoder starting with phrase-bigram decoding using word-internal triphones and finishing with a phrase-trigram decoding using MLLR-adapted crossword models.

Acoustic Modeling in the Philips Hub-4 Continuous-Speech Recognition System - Haeb-Umbach, Reinhold; Aubert, Xavier L.; Beyerlein, Peter; Klakow, Dietrich; Ullrich, Meinhard; Wendemuth, Andreas; Wilcox, Patricia (DARPA Broadcast News Transcription and Understanding Workshop, Landsdowne)
In this paper we describe some characteristics of the acoustic modeling used in the Philips continuous-speech recognition system for the DARPA Hub-4 1997 evaluation, which are related to robustness issues. We aimed at a conceptually simple system: We trained two model sets on 70 hours of the Hub-4 training data, one for within-word and one for cross-word decoding. These model sets were used for both genders and all environmental conditions. In order to be able to do so, channel normalization (mean, variance normalization) and speaker normalization (vocal tract length normalization, realized by an appropriate shift of the center frequencies of the mel filter bank) have been applied, as well as adaptation techniques. MLLR-based unsupervised batch adaptation on clusters of segments was conducted both after a first within-word decoding and a cross-word decoding pass. The training strategy and the effects of the various normalization and adaptation techniques will be discussed in the paper.

Language-Model Investigations related to Broadcast News - Klakow, Dietrich; Aubert, Xavier L.; Haeb-Umbach, Reinhold; Beyerlein, Peter; Ullrich, Meinhard; Wendemuth, Andreas; Wilcox, Patricia (DARPA Broadcast News Transcription and Understanding Workshop, Landsdowne)
In this paper we present some experiments that have been performed while developing language models for the PHILIPS Broadcast News system. Three main issues will be discussed: construction of phrases, adaptation of remote corpora to this task, and the combination of the different models. Also, perplexities on the 1997 evaluation data are reported.