Improving reverberant VTS for hands-free robust speech recognition

Wang, Y.-Q.; Gales, Mark J. F.

doi:10.1109/asru.2011.6163915

Cited by 8 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The convolutive nature of reverberation induces a long-term correlation between a current observation and past observations of reverberant speech. This longterm correlation has been exploited to mitigate the effect of reverberation directly on the speech signal (i.e., speech [9][10][11][12] or feature [13,14] dereverberation) or on the acoustic model used for recognition [15,16]. The REVERB challenge [17] was organized to evaluate recent progress in the field of reverberant speech enhancement (SE) and recognition.…”

Section: Y(t) = H(t) * S(t) + N(t)mentioning

confidence: 99%

Strategies for distant speech recognitionin reverberant environments

Delcroix

Yoshioka

Ogawa

et al. 2015

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Reverberation and noise are known to severely affect the automatic speech recognition (ASR) performance of speech recorded by distant microphones. Therefore, we must deal with reverberation if we are to realize high-performance hands-free speech recognition. In this paper, we review a recognition system that we developed at our laboratory to deal with reverberant speech. The system consists of a speech enhancement (SE) front-end that employs long-term linear prediction-based dereverberation followed by noise reduction. We combine our SE front-end with an ASR back-end that uses neural networks for acoustic and language modeling. The proposed system achieved top scores on the ASR task of the REVERB challenge. This paper describes the different technologies used in our system and presents detailed experimental results that justify our implementation choices and may provide hints for designing distant ASR systems.

show abstract

Section: Y(t) = H(t) * S(t) + N(t)mentioning

confidence: 99%

Strategies for distant speech recognitionin reverberant environments

Delcroix

Yoshioka

Ogawa

et al. 2015

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…7 Bayesian network representation of reverberant VTS (Subsection 6.6.1) a before and b after approximation via an extended observation vector. The figure is based on [15] performs an online adaptation based on the best partial path [15]. It should be pointed out here that there is a variety of other approximations to the statistics of the logsum of (mixtures of ) Gaussian random variables (as seen in Subsections 4.2, 4.5, 6.1, 6.2, 6.6.1), ranging from different PMC methods [10] to maximum [64], piecewise linear [65], and other analytical approximations [66][67][68][69][70].…”

Section: Convolutive Model Adaptationmentioning

confidence: 99%

“…In this article, we review and examine for several uncertainty decoding [1][2][3][4][5], missing feature [6][7][8][9], and model adaptation techniques [10][11][12][13][14][15][16][17][18][19] how their compensation rules can be formulated as an approximated or modified Bayesian decoding rule. In order to illustrate the formalism, we also present the corresponding Bayesian network representations.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian view on acoustic model-based techniques for robust speech recognition

Maas

Huemmer

Sehr

et al. 2015

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This article provides a unifying Bayesian view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By identifying and converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules. We thus summarize the various approaches as approximations or modifications of the same Bayesian decoding rule leading to a unified view on known derivations as well as to new formulations for certain approaches.

show abstract

“…Assuming the speech and the noise LMPSCs to be mutually independent, the predictive PDF may be factorized as (8) Thus, the first required model is an a priori model to stochastically describe the trajectory of the LMPSC vectors of the clean speech signal and the noise signal, respectively.…”

Section: Overview Of Feature Enhancementmentioning

confidence: 99%

“…The approach proposed by Wang and Gales [8], [9] also attempts a dynamic compensation, however, quite differently. By defining a spliced vector comprising several adjacent feature vectors, a frame-by-frame compensation is achieved, thus reducing the computational effort.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition

Leutnant

Krueger

Haeb‐Umbach

2013

IEEE Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

Abstract-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.Index Terms-Robust automatic speech recognition, modelbased BAYESIAN feature enhancement, observation model for reverberant and noisy speech, recursive observation model.

show abstract

Improving reverberant VTS for hands-free robust speech recognition

Cited by 8 publications

References 16 publications

Strategies for distant speech recognitionin reverberant environments

Strategies for distant speech recognitionin reverberant environments

A Bayesian view on acoustic model-based techniques for robust speech recognition

Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition

Contact Info

Product

Resources

About