Optimal estimators for spectral restoration of noisy speech

Porter, J.; Boll, S.

doi:10.1109/icassp.1984.1172545

Cited by 98 publications

(87 citation statements)

References 8 publications

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“…In speech enhancement applications, estimators which minimize the mean-squared error of the LSA have been found advantageous to MMSE spectral estimators [2,3,24]. An MMSE-LSA estimator is obtained by substituting into (12) …”

Section: Mmse Log-spectral Amplitude Estimationmentioning

confidence: 99%

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Cohen

2006

Signal Processing

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In this paper, we develop and evaluate speech enhancement algorithms, which are based on supergaussian generalized autoregressive conditional heteroscedasticity (GARCH) models in the short-time Fourier transform (STFT) domain. We consider three different statistical models, two fidelity criteria, and two approaches for the estimation of the variances of the STFT coefficients. The statistical model is either Gaussian, Gamma or Laplacian; the fidelity criteria include minimum mean-squared error (MMSE) of the STFT coefficients and MMSE of the log-spectral amplitude (LSA); the spectral variance is estimated based on either the proposed GARCH models or the decision-directed method of Ephraim and Malah. We show that estimating the variance by the GARCH modeling method yields lower log-spectral distortion and higher perceptual evaluation of speech quality scores (PESQ, ITU-T P.862) than by using the decisiondirected method, whether the presumed statistical model is Gaussian, Gamma or Laplacian, and whether the fidelity criterion is MMSE of the STFT coefficients or MMSE of the LSA. furthermore while a gaussian model is inferior to the supergaussian models when USING the decision-directed method, the Gaussian model is superior when using the garch modeling method. r 2005 Published by Elsevier B.V.

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Section: Mmse Log-spectral Amplitude Estimationmentioning

confidence: 99%

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Cohen

2006

Signal Processing

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“…Parameter values of these models are estimated from samples of speech in the testing environments, and either the features of the incoming speech or the internally-stored representations of speech in the system are modified. Typical structural models for adaptation to acoustical variability assume that speech is corrupted either by additive noise with an unknown power spectrum (Porter & Boll, 1984;Ephraim, 1992;Erell & Weintraub, 1990;Gales & Young, 1992;Lockwood, Boudy, et al, 1992;Bellegarda, de Souza, et al, 1992), or by a combination of additive noise and linear filtering (Acero & Stern, 1990). Much of the early work in robust recognition involved a re-implementation of techniques developed to remove additive noise for the purpose of speech enhancement, as reviewed in section 10.3.…”

Section: Optimal Parameter Estimationmentioning

confidence: 99%

“…This is the underlying reason why ad hoc operations like clipping are necessary. Within the framework of spectral magnitude estimation two major improvements are: (i) modeling of realistic a priori statistical distributions of speech and noise spectral magnitude coefficients (Ephraim & Malah, 1984), (ii) minimizing the estimation error in a domain which is perceptually more relevant than the power spectral domain (e.g., log magnitude domain) (Porter & Boll, 1984;Ephraim & Malah, 1985;Van Compernolle, 1989).…”

Section: Minimum Mean Square Error Estimatorsmentioning

confidence: 99%

Survey of the State of the Art in Human Language Technology

Cohen¹,

Varile²,

Zampolli³

et al. 2000

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133

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“…For example, Boll [3] and Beroufi et al [2] introduced the spectral subtraction of DFT coefficients, and Porter and Boll [11] used MMSE techniques to estimate the DFT coefficients of corrupted speech. Spectral equalization to compensate for convolved distortions was introduced by Stockham et al [13].…”

Section: Introductionmentioning

confidence: 99%

Towards Environment-Independent Spoken Language Systems

Acero¹,

Stern²

1990

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In this paper we discuss recent results from our efforts to make SPHINX, the CMU continuous-speech speakerindependent recognition system, robust to changes in the environment. To deal with differences in noise level and spectral tilt between close-talking and desk-top microphones, we describe two novel methods based on additive corrections in the cepstral domain. In the first algorithm, an additive correction is imposed that depends on the instantaneous SNR of the signal. In the second technique, EM techniques are used to best match the cepstral vectors of the input utterances to the ensemble of codebook entries representing a standard acoustical ambience. Use of these algorithms dramatically improves recognition accuracy when the system is tested on a microphone other than the one on which it was trained.

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Optimal estimators for spectral restoration of noisy speech

Cited by 98 publications

References 8 publications

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Survey of the State of the Art in Human Language Technology

Towards Environment-Independent Spoken Language Systems

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