Method of noise estimation using incremental bayes learning

Details Method of noise estimation using incremental bayes learning Method of noise estimation using incremental bayes learning

2007-01-16

2007-01-16

Chin, Vivian (Department: 2615)

Data processing: speech signal processing, linguistics, language

Speech signal processing

For storage or transmission

C704S228000, C704S233000

Reexamination Certificate

active

10403638

ABSTRACT:
A method and apparatus estimate additive noise in a noisy signal using incremental Bayes learning, where a time-varying noise prior distribution is assumed and hyperparameters (mean and variance) are updated recursively using an approximation for posterior computed at the preceding time step. The additive noise in time domain is represented in the log-spectrum or cepstrum domain before applying incremental Bayes learning. The results of both the mean and variance estimates for the noise for each of separate frames are used to perform speech feature enhancement in the same log-spectrum or cepstrum domain.

REFERENCES:
patent: 4918735 (1990-04-01), Morito
patent: 5012519 (1991-04-01), Adlersberg et al.
patent: 5148489 (1992-09-01), Erell et al.
patent: 5604839 (1997-02-01), Acero et al.
patent: 5727124 (1998-03-01), Lee et al.
patent: 5924065 (1999-07-01), Eberman et al.
patent: 6092045 (2000-07-01), Stubley et al.
patent: 6343267 (2002-01-01), Kuhn et al.
patent: 6778954 (2004-08-01), Kim et al.
patent: 6944590 (2005-09-01), Deng et al.
patent: 2003/0055640 (2003-03-01), Burshtein et al.
patent: 2003/0191637 (2003-10-01), Deng
patent: 2003/0216911 (2003-11-01), Deng et al.
patent: 2004/0064313 (2004-04-01), Shimosakoda
patent: 2004/0064314 (2004-04-01), Aubert et al.
Brendan, J. et al. ALGONQUIN: Iterating Laplace's Method to Remove Multiple Types of Acoustic Distortion for Robust Speech Recognition. Proc. of the Eurospeech Conference, Aalborg, Denmark. Sep. 2001.
Gauvain, Jean-Luc et al. Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains. IEEE Transactions on Speech and Audio Processing, vol. 2, No. 2,pp. 291-298, Apr. 1994.
Attias, Hagai et al. A New Method for Speech Denoising and Robust Speech Recognition Uising Probabilistic Models for Clean Speech and for Noise. Proceedings Eurosppech, 2001, pp. 1903-1906.
Acero et al., “Recursive estimation of nonstationary noise using a nonlinear model with iterative stochastic approximation,” Proc. ASRU Workshop, Trento, Italy, Dec. 2001, 4 pages.
Acero et al., “Log-domain speech feature enhancement using sequential MAP noise estimation and a phase-sensitive model of the acoustic environment,” Proc. ICSLP, Denver CO, Sep. 2002, pp. 192-195.
Acero et al., “A Bayesian approach to speech feature enhancement using the dynamic cepstral prior,” Proc. ICASSP, Orlando, Florida, May 2002, pp. 829-832.
Deng et al. “A nonlinear observation model for removing noise from corrupted speech log Mel-spectral energies,” Proc. ICSLP, 2002, pp. 182-185.
Lee et al. “On-line adaptive learning of the continuous density HMM based on approximate recursive Bayes estimate,” IEEE Trans. Speech Audio Proc., vol. 5, No. 2, Mar. 1997, pp. 161-172.
J. Spragins. “A note on the iterative application of Bayes' rule,” IEEE Trans. Inform. Theory, vol. 11, No. 4, pp. 544-549.
Deng et al., “Recursive noise estimation using iterative stochastic approximation for stereo-based robust speech recognition”, Proc. IEEE, Automatic Speech Recognition and Understanding, pp. 81-84, Dec. 9, 2001, XP002259233.
Huo et al., “On-line Adaptive Learning of the Continuous Density Hidden Markov Model Based on Approximate Recursive Bayes Estimate”, Proc. IEEE, Speech and Audio Processing, vol. 5, No. 2, pp. 161-172, Mar. 2, 1997, XP000771954.
Droppo et al., “A Nonlinear Observation Model for Removing Noise From Corrupted Speech Log Mel-Specteral Energies”, ICSLP 2002: 7th Int. Conf. On Spoken Language Processing, Denver, CO, Sep. 16-20, 2002, pp. 1569-1572, XP008025395.
U.S. Appl. No. 10/117,142, filed Apr. 5, 2002, James G. Droppo et al.
U.S. Appl. No. 09/668,764, filed Oct. 16, 2000, Li Deng et al.
U.S. Appl. No. 09/688,950, filed Oct. 16, 2000, Li Deng et al.
Gauvain et al. “Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains,” Apr. 1994, IEEE Transactions on Speech and Audio Processing, vol. 2, No. 2, pp. 291-298.
Li Deng and Jeff Ma, “Spontaneous speech recognition using a statistical coarticulatory model for the vocal-tract-resonance dynamics,” J. Acoust. Soc. Am. 108(5), Pt. 1, Nov. 2002.
Jeff Ma and Li Deng, “A path-stack algorithm for optimizing dynamic regimes in a statistical hidden dynamic model of speech,” Computer speech and Language 2000, 00, 1-14.
“Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition,” Brendan J. Frey, et al., Neural Information Processing System Conference, 2001, pp. 1165-1121.
“Speech Denoising and Dereverberation Using Probabilistic Models,” Hagai Attias, et al. , Advance in NIPS, vol. 13, 2000 pp. 758-764.
“Statistical-Model-Based Speech Enhancement Systems,” Proc. of IEEE, vol. 80, No. 10, Oct. 1992, pp. 1526.
“HMM-Based Strategies for Enhancement of Speech Signals Embedded in Nonstationary Noise,” Hossein Sameti, IEEE Trans. Speech Audio Processing, vol. 6, No. 5, Sep. 1998, pp. 445-455.
“Model-based Compensation of the Additive Noise for Continuous Speech Recognition,” J.C. Segura, et al., Eurospeech 2001.
“Large-Vocabulary Speech Recognition Under Adverse Acoustic Environments,” Li Deng, et al., Proc. ICSLP, vol. 3, 2000, pp. 806-809.
“A Compact Model for Speaker-Adaptive Training,” Anastasakos, T., et al., BBN Systems and Technologies, pp. 1137-1140 (undated).
“Suppression of Acoustic Noise in Speech Using Spectral Subtraction,” Boll, S. F., IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-27, No. 2, pp. 113-120 (Apr. 1979).
“Experiments With a Nonlinear Spectral Subtractor (NSS) , Hidden Markov Models and the Projection, for Robust Speech Recognition in Cars,” Lockwood, P. et al., Speech Communication 11, pp. 215-228 (1992).
“A Spectral Subtraction Algorithm for Suppression of Acoustic Noise in Speech,” Boll, S.F., IEEE International Conference on Acoustics, Speech & Signal Processing, pp. 200-203 (Apr. 2-4, 1979).
“Enhancement of Speech Corrupted by Acoustic Noise,” Berouti, M. et al., IEEE International Conference on Acoustics, Speech & Signal Processing, pp. 208-211 (Apr. 2-4, 1979).
“Acoustical and Environmental Robustness in Automatic Speech Recognition,” Acero, A., Department of Electrical and Computer Engineering, Carnegie Mellon University, pp. 1-141 (Sep. 13, 1990).
“Speech Recognition in Noisy Environments,” Pedro J. Moreno, Ph.D thesis, Carnegie Mellon University, 1996.
“A New Method for Speech Denoising and Robust Speech Recognition Using Probabilistic Models for Clean Speech and for Noise,” Hagai Attias, et al., Proc. Eurospeech, 2001, pp. 1903-1906.
“HMM Adaptation Using Vector Taylor Series for Noisy Speech Recognition,” Alex Acero, et al., Proc. ICSLP, vol. 3, 2000, pp. 869-872.
“Sequential Noise Estimation with Optimal Forgetting for Robust Speech Recognition,” Mohomed Afify, et al., Proc. ICASSP, vol. 1, 2001, pp. 229-232.
“High-Performance Robust Speech Recognition Using Stereo Training Data,” Li Deng, et al., Proc. ICASSP, vol. 1, 2001, pp. 301-304.
“ALGONQUIN: Iterating Laplace's Method to Remove Multiple Types of Acoustic Distortion for Robust Speech Recognition,” Brendan J. Frey, et al., Proc. Eurospeech, Sep. 2001, Aalborg, Denmark.
“Nonstationary Environment Compensation Based on Sequential Estimation,” Nam Soo Kim, IEEE Signal Processing Letters, vol. 5, 1998, pp. 57-60.
“On-line Estimation of Hidden Markov Model Parameters Based on the Kullback-Leibler Information Measure,” Vikram Krishnamurthy, et al., IEEE Trans. Sig. Proc., vol. 41, 1993, pp. 2557-2573.
“A Vector Taylor Series Approach for Environment-Independent Speech Recognition,” Pedro J. Moreno, ICASSP, vol. 1, 1996, pp. 733-736.
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