Probabilistic optimum filtering for robust speech recognition

Neumeyer, Leonardo; Weintraub, M.

doi:10.1109/icassp.1994.389267

Cited by 76 publications

(39 citation statements)

References 13 publications

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“…Some of these techniques have attempted to address the problem by applying compensation factors to the incoming cepstrum vectors (e.g. Acero and Stern, 1990;Liu et al, 1994;Neumeyer and Weintraub, 1994;Moreno et al, 1996) while other techniques have modi®ed parameters characterizing the statistical model of speech sounds such as the means and covariances of the distributions of HMMs (e.g. Gales, 1995;Sankar and Lee, 1995).…”

Section: Introductionmentioning

confidence: 99%

Data-driven environmental compensation for speech recognition: A unified approach

Moreno¹,

Raj²,

Stern³

1998

Speech Communication

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Environmental robustness for automatic speech recognition systems based on parameter modi®cation can be accomplished in two complementary ways. One approach is to modify the incoming features of environmentally-degraded speech to more closely resemble the features of the (normally undegraded) speech used to train the classi®er. The other approach is to modifying the internal statistical representations of speech features used by the classi®er to more closely resemble the features representing degraded speech in a particular target environment. This paper attempts to unify these two approaches to robust speech recognition by presenting several techniques that share the same basic assumptions and internal structure while diering in whether they modify the features of incoming speech or whether they modify the statistics of the classi®er itself. We present the multivaRiate gAussian-based cepsTral normaliZation (RATZ) family of algorithms which modify incoming cepstral features, along with the STAR (STAtistical Reestimation) family of algorithms, which modify the internal statistics of the classi®er. Both types of algorithms are data driven, in that they make use of a certain amount of adaptation data for learning compensation parameters. The algorithms were evaluated using the SPHINX-II speech recognition system on subsets of the Wall Street Journal database. While all algorithms demonstrated improved recognition accuracy compared to previous algorithms, the STAR family of algorithms tended to provide lower error rates than the RATZ family of algorithms as the SNR was decreased. Ó 1998 Elsevier Science B.V. All rights reserved. Re Âsume ÂDans les syst emes de reconnaissance de la parole, la robustesse a l'environnement par adaptation des param etres peutêtre obtenue de deux fac Ëons compl ementaires. Une premi ere approche consiste a modi®er les param etres acoustiques de la parole d egrad ee par l'environnement de fac Ëon a ressembler aux param etres de la parole (habituellement non d egrad ee) qui a et e utilis ee lors de l'entraõnement. La deuxi eme solution est de modi®er les param etres statistiques internes au reconnaisseur de fac Ëon a mieux repr esenter les caract eristiques de la parole d egrad ee dans un environnement cible particulier. Le pr esent papier tente d'uni®er ces deux approches de reconnaissance robuste de la parole en pr esentant plusieurs techniques qui partagent les mêmes hypoth eses de base et la même structure, tout en di erant dans le choix de savoir si elles modi®ent les param etres d'entr ee ou les param etres statistiques du reconnaisseur. Nous pr esentons ici la famille d'algorithmes bas es sur la normalisation cepstrale gaussienne multi-variable (RATZ) qui modi®ent les caract eristiques cepstrales d'entr ee, ainsi que les algorithmes STAR (re-estimation statistique), qui modi®ent les param etres internes du reconnaisseur. Les deux types d'algorithmes sont bas es sur les donn ees et utilisent une certaine quantit e de donn ee d'adaptation pour estimer les param etres de compen...

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Section: Introductionmentioning

confidence: 99%

Data-driven environmental compensation for speech recognition: A unified approach

Moreno¹,

Raj²,

Stern³

1998

Speech Communication

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“…In the log-spectral domain this can be expressed as: (2) or in more general terms: (3) where equals . We refer to in equation (3) as the environment function.…”

Section: A Model Of the Environmentmentioning

confidence: 99%

“…speech databases that are simultaneously recorded in high-quality and degraded environments (e.g. [1] [2]). Other algorithms make use of large amounts of adaptation data from the degraded environment (e.g.…”

Section: Introductionmentioning

confidence: 99%

Cepstral compensation by polynomial approximation for environment-independent speech recognition

Raj

Gouvêa²,

Moreno³

et al.

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96

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Speech recognition systems perform poorly on speech degraded by even simple effects such as linear filtering and additive noise. One possible solution to this problem is to modify the probability density function (PDF) of clean speech to account for the effects of the degradation. However, even for the case of linear filtering and additive noise, it is extremely difficult to do this analytically. Previously attempted analytical solutions to the problem of noisy speech recognition have either used an overly-simplified mathematical description of the effects of noise on the statistics of speech, or they have relied on the availability of large environmentspecific adaptation sets. Some of the previous methods required the use of adaptation data that consists of simultaneously-recorded or "stereo" recordings of clean and degraded speech. In this paper we introduce an approximation-based method to compute the effects of the environment on the parameters of the PDF of clean speech.In this work, we perform compensation by Vector Polynomial approximationS (VPS) for the effects of linear filtering and additive noise on the clean speech. We also estimate the parameters of the environment, namely the noise and the channel, by using piecewiselinear approximations of these effects.We evaluate the performance of this method (VPS) using the CMU SPHINX-II system and the 100-word alphanumeric CENSUS database. Performance is evaluated at several SNRs, with artificial white Gaussian noise added to the database. VPS provides improvements of up to 15 percent in relative recognition accuracy.

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“…speech databases that are simultaneously recorded in high-quality and degraded environments (e.g. [1] [2]). Other algorithms make use of non-simultaneously-recorded adaptation data from the degraded environment (e.g.…”

Section: Introductionmentioning

confidence: 99%

A vector Taylor series approach for environment-independent speech recognition

Moreno

Raj

Stern³

1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings

342

231

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In this paper we introduce a new analytical approach to environment compensation for speech recognition. Previous attempts at solving analytically the problem of noisy speech recognition have either used an overly-simplified mathematical description of the effects of noise on the statistics of speech or they have relied on the availability of large environment-specific adaptation sets. Some of the previous methods required the use of adaptation data that consists of simultaneouslyrecorded or "stereo" recordings of clean and degraded speech.In this work we introduce the use of a Vector Taylor series (VTS) expansion to characterize efficiently and accurately the effects on speech statistics of unknown additive noise and unknown linear filtering in a transmission channel. The VTS approach is computationally efficient. It can be applied either to the incoming speech feature vectors, or to the statistics representing these vectors. In the first case the speech is compensated and then recognized; in the second case HMM statistics are modified using the VTS formulation. Both approaches use only the actual speech segment being recognized to compute the parameters required for environmental compensation.We evaluate the performance of two implementations of VTS algorithms using the CMU SPHINX-II system on the 100-word alphanumeric CENSUS database and on the 1993 5000-word ARPA Wall Street Journal database. Artificial white Gaussian noise is added to both databases. The VTS approaches provide significant improvements in recognition accuracy compared to previous algorithms. INTRODUCTIONAs speech recognition systems become more accurate and more sophisticated, robustness to noise, channel, and other environmental effects becomes increasingly important. In the past few years, researchers at CMU and other sites have developed a series of techniques to address this problem. Many of these environment compensation algorithms take advantage of the availability of "stereo data", i.e. speech databases that are simultaneously recorded in high-quality and degraded environments (e.g. [1][2]). Other algorithms make use of non-simultaneously-recorded adaptation data from the degraded environment (e.g. [5]

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Probabilistic optimum filtering for robust speech recognition

Cited by 76 publications

References 13 publications

Data-driven environmental compensation for speech recognition: A unified approach

Data-driven environmental compensation for speech recognition: A unified approach

Cepstral compensation by polynomial approximation for environment-independent speech recognition

A vector Taylor series approach for environment-independent speech recognition

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