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

Simulation of Ad Hoc Routing Protocols using OMNeT++ - Sommer, Christoph; Dietrich, Isabel; Dressler, Falko (Mobile Networks and Applications)

An evaluation of object oriented example programs in introductory programming textbooks - Börstler, Jürgen; S. Hall, Mark; Nordström, Marie; H. Paterson, James; Sanders, Kate; Schulte, Carsten; Thomas, Lynda (SIGCSE Bulletin)