Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Reexamination Certificate
2006-01-24
2006-01-24
McFadden, Susan (Department: 2655)
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
C704S245000, C704S226000
Reexamination Certificate
active
06990447
ABSTRACT:
A probability distribution for speech model parameters, such as auto-regression parameters, is used to identify a distribution of denoised values from a noisy signal. Under one embodiment, the probability distributions of the speech model parameters and the denoised values are adjusted to improve a variational inference so that the variational inference better approximates the joint probability of the speech model parameters and the denoised values given a noisy signal. In some embodiments, this improvement is performed during an expectation step in an expectation-maximization algorithm. The statistical model can also be used to identify an average spectrum for the clean signal and this average spectrum may be provided to a speech recognizer instead of the estimate of the clean signal.
REFERENCES:
patent: 2002/0059065 (2002-05-01), Rajan
Vassilios V. Digalakis, Online Adaptation of Hidden Markov Models Using Incremental Estimation Algorithms, IEEE Transactions on Speech and Audio Processing, May 1999.
D. Burshtein, Joint Maximum Likelihood Estimation of Pitch and AR Parameters using the EM Algorithm, IEEE ICASSP, 1990.
Yunxin Zhao, Spectrum Estimation of Short-Time Stationary Signals in Additive Noise and Channel Distortion, IEEE Transactions on Signal Processing, Jul. 2001.
Marc Fayolle and Jerome Idier, EM Parameter Estimation for a Piecewise AR, IEEE ICASSP 1997.
Feder, Weinstein and Oppenheim, A new class of Sequential and Adaptive Algorithms with Application to Noise Cancellation, IEEE ICASSP, 1988.
Lawrence, Variational Inference in Probabilistic Models, Cambridge University, PhD Thesis, Jan. 2000.
U.S. Appl. No. 09/812,524, filed Mar. 20, 2001, Frey et al.
A.P. Varga and R.K. Moore, “Hidden Markov Model Decomposition of Speech and Noise,” in Proceedings of the International Conference on Acoustics, Speech and Signal Processing, IEEE Press., pp. 845-848 (1990).
S. Boll, “Suppression of Acoustic Noise in Speech Using Spectral Subtraction, ” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 27, pp. 114-120 (1979).
L. Deng, A. Acero, M. Plumpe & X.D. Huang, “Large-Vocabulary Speech Recognition Under Adverse Acoustic Environments, ” in Proceedings of the International Conference on Spoken Language Processing, pp. 806-809 (Oct. 2000).
A. Acero, L. Deng, T. Kristjansson and J. Zhang, “HMM Adaptation Using Vector Taylor Series for Noisy Speech Recognition, ” in Proceedings of the International Conference on Spoken Language Processing, pp. 869-872 (Oct. 2000).
Y. Ephraim, “Statistical-Model-Based Speech Enhancement Systems, ” Proc. IEEE, 80(10):1526-1555 (1992).
M.S. Brandstein, “On the Use of Explicit Speech Modeling in Microphone Array Application, ” In Proc. ICASSP, pp. 3613-3616 (1998).
A. Dembo and O. Zeitouni, “Maximum A Posteriori Estimation of Time-Varying ARMA Processes from Noisy Observations,” IEEE Trans. Acoustics, Speech and Signal Processing, 36(4):471-476 (1988).
P. Moreno, “Speech Recognition in Noisy Environments,” Carnegie Mellon University, Pittsburgh, 9, PA, pp. 1-130 (1996).
B. Frey, “Variational Inference and Learning in Graphical Models,” University of Illinois at urbana, 6 pages (updated).
Y. Ephraim and R. Gray, “A Unified Approach for Encoding Clean and Noisy Sources by Means of Waveform and Autoregressive Model Vector Quantization,” IEEE Transactions on Information Theory, vol. 34, No. 4, pp. 826-834 (Jul. 1988).
R. Neal and G. Hinton, “A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants,” pp. 1-14 (updated).
J. Lim and A. Oppenheim, “All-Pole Modeling of Degraded Speech,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-26, No. 3, pp. 197-210 (Jun. 1978).
Y. Ephraim, “A Bayesian Estimation Approach for Speech Enhancement Using Hidden Markov Models,” IEEE Transactions on Signal Processing, vol. 40, No. 4, pp. 725-735 (Apr. 1992).
“Noise Reduction” downloaded from http://www.ind.rwth-aachen.de/research
oise—reduction.html, pp. 1-11 (Oct. 3, 2001).
A. Acero, “Acoustical and Environmental Robustness in Automatic Speech Recognition,” Department of Electrical and Computer Engineering, pp. 1-141 (Sep. 13, 1990).
B. Frey et al., “Algonquin: Iterating Laplace's Method to Remove Multiple Types of Acoustic Distortion for Robust Speech Recognition,” In Proceedings of Eurospeech, 4 pages (2001).
Acero Alejandro
Attias Hagai
Deng Li
Platt John Carlton
Magee Theodore M.
McFadden Susan
Microsoft Corportion
Rivero Minerva
Westman Champlin & Kelly P.A.
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