MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems - von Neumann, Thilo; Boeddeker, Christoph; Delcroix, Marc; Haeb-Umbach, Reinhold (Proc. CHiME 2023 Workshop on Speech Processing in Everyday Environments)
MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription systems. It provides a unified interface for the computation of commonly used Word Error Rates (WERs), specifically cpWER, ORC WER and MIMO WER along other WER definitions. We extend the cpWER computation by a temporal constraint to ensure that only words are identified as correct when the temporal alignment is plausible. This leads to a better quality of the matching of the hypothesis string to the reference string that more closely resembles the actual transcription quality, and a system is penalized if it provides poor time annotations. Since word-level timing information is often not available, we present a way to approximate exact word-level timings from segment-level timings (e.g., a sentence) and show that the approximation leads to a similar WER as a matching with exact word-level annotations. At the same time, the time constraint leads to a speedup of the matching algorithm, which outweighs the additional overhead caused by processing the time stamps.
Keywords: Speech Recognition; Word Error Rate; Meeting Transcription

Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers - von Neumann, Thilo; Kinoshita, Keisuke; Boeddeker, Christoph; Delcroix, Marc; Haeb-Umbach, Reinhold (Interspeech 2021)
Automatic transcription of meetings requires handling of overlapped speech, which calls for continuous speech separation (CSS) systems. The uPIT criterion was proposed for utterance-level separation with neural networks and introduces the constraint that the total number of speakers must not exceed the number of output channels. When processing meeting-like data in a segment-wise manner, i.e., by separating overlapping segments independently and stitching adjacent segments to continuous output streams, this constraint has to be fulfilled for any segment. In this contribution, we show that this constraint can be significantly relaxed. We propose a novel graph-based PIT criterion, which casts the assignment of utterances to output channels in a graph coloring problem. It only requires that the number of concurrently active speakers must not exceed the number of output channels. As a consequence, the system can process an arbitrary number of speakers and arbitrarily long segments and thus can handle more diverse scenarios. Further, the stitching algorithm for obtaining a consistent output order in neighboring segments is of less importance and can even be eliminated completely, not the least reducing the computational effort. Experiments on meeting-style WSJ data show improvements in recognition performance over using the uPIT criterion.
Keywords: Continuous speech separation; automatic speech recognition; overlapped speech; permutation invariant training

Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions - Heymann, Jahn; Haeb-Umbach, Reinhold; Golik, P.; Schlueter, R. (Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on)
The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data.
Keywords: codecs; signal denoising; speech recognition; Bayesian feature enhancement; denoising autoencoder; reverberant ASR; single-channel speech recognition; speaker to microphone distances; unsupervised adaptation; Adaptation models; Noise reduction; Reverberation; Speech; Speech recognition; Training; deep neuronal networks; denoising autoencoder; feature enhancement; robust speech recognition

A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE/ACM Transactions on Audio, Speech, and Language Processing)
In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data.
Keywords: computational complexity; reverberation; speech recognition; automatic speech recognition; background noise; clean speech; computational complexity; energy compensation; logarithmic mel power spectral domain; mel frequency cepstral coefficients; microphone input signals; model-based feature compensation schemes; noisy reverberant speech automatic recognition; noisy reverberant speech features; reverberation; Atmospheric modeling; Computational modeling; Noise; Noise measurement; Reverberation; Speech; Vectors; Model-based feature compensation; observation model for reverberant and noisy speech; recursive observation model; robust automatic speech recognition

An Overview of Noise-Robust Automatic Speech Recognition - Li, Jinyu; Deng, Li; Gong, Yifan; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech and Language Processing)
New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed.
Keywords: Speech recognition; compensation; distortion modeling; joint model training; noise; robustness; uncertainty processing

GMM-based significance decoding - Abdelaziz, Ahmed H.; Zeiler, Steffen; Kolossa, Dorothea; Leutnant, Volker; Haeb-Umbach, Reinhold (Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on)
The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios.
Keywords: Bayes methods; Gaussian processes; convolution; decision theory; decoding; noise; reverberation; speech coding; speech recognition; Bayesian decision rule; GMM; Gaussian mixture models; additive noise scenarios; automatic speech recognition systems; convolutive noise scenarios; decoding approach; mathematical framework; reverberant environments; significance decoding; speech feature estimation; uncertainty-of-observation techniques; Hidden Markov models; Maximum likelihood decoding; Noise; Speech; Speech recognition; Uncertainty; Uncertainty-of-observation; modified imputation; noise robust speech recognition; significance decoding; uncertainty decoding

Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data.
Keywords: Bayes methods; compensation; error statistics; reverberation; speech recognition; Bayesian feature enhancement; background noise; clean speech feature vectors; compensation; connected digits recognition task; error statistics; memory requirements; noisy reverberant data; posteriori probability density function; recursive formulation; reverberant logarithmic mel power spectral coefficients; robust automatic speech recognition; signal-to-noise ratios; time-variant observation; word error rate reduction; Robust automatic speech recognition; model-based Bayesian feature enhancement; observation model for reverberant and noisy speech; recursive observation model

A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference on)
In this work, an observation model for the joint compensation of noise and reverberation in the logarithmic mel power spectral density domain is considered. It relates the features of the noisy reverberant speech to those of the non-reverberant speech and the noise. In contrast to enhancement of features only corrupted by reverberation (reverberant features), enhancement of noisy reverberant features requires a more sophisticated model for the error introduced by the proposed observation model. In a first consideration, it will be shown that this error is highly dependent on the instantaneous ratio of the power of reverberant speech to the power of the noise and, moreover, sensitive to the phase between reverberant speech and noise in the short-time discrete Fourier domain. Afterwards, a statistically motivated approach will be presented allowing for the model of the observation error to be inferred from the error model previously used for the reverberation only case. Finally, the developed observation error model will be utilized in a Bayesian feature enhancement scheme, leading to improvements in word accuracy on the AURORA5 database.
Keywords: Robust Automatic Speech Recognition; Bayesian feature enhancement; observation model for reverberant and noisy speech

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

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