Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
2007-10-30
2007-10-30
Lerner, Martin (Department: 2626)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S233000, C704S240000
Reexamination Certificate
active
11642389
ABSTRACT:
A method and apparatus are provided for determining uncertainty in noise reduction based on a parametric model of speech distortion. The method is first used to reduce noise in a noisy signal. In particular, noise is reduced from a representation of a portion of a noisy signal to produce a representation of a cleaned signal by utilizing an acoustic environment model. The uncertainty associated with the noise reduction process is then computed. In one embodiment, the uncertainty of the noise reduction process is used, in conjunction with the noise-reduced signal, to decode a pattern state.
REFERENCES:
patent: 4897878 (1990-01-01), Boll et al.
patent: 4905286 (1990-02-01), Sedgwick et al.
patent: 5148489 (1992-09-01), Erell et al.
patent: 5604839 (1997-02-01), Acero et al.
patent: 5924065 (1999-07-01), Eberman et al.
patent: 6098040 (2000-08-01), Petroni et al.
patent: 6173258 (2001-01-01), Menendez-Pidal et al.
patent: 6202047 (2001-03-01), Ephraim et al.
patent: 6418411 (2002-07-01), Gong
patent: 6577997 (2003-06-01), Gong
patent: 6633842 (2003-10-01), Gong
patent: 6633843 (2003-10-01), Gong
patent: 6763075 (2004-07-01), Zhengdi et al.
patent: 6865531 (2005-03-01), Huang
patent: 6876966 (2005-04-01), Deng et al.
patent: 6898566 (2005-05-01), Benyassine et al.
patent: 6915259 (2005-07-01), Rigazio et al.
patent: 6944590 (2005-09-01), Deng et al.
patent: 6959276 (2005-10-01), Droppo et al.
patent: 6980952 (2005-12-01), Gong
patent: 6985858 (2006-01-01), Frey et al.
patent: 6990447 (2006-01-01), Attias et al.
patent: 7003455 (2006-02-01), Deng et al.
patent: 7103540 (2006-09-01), Droppo et al.
patent: 7107210 (2006-09-01), Deng et al.
patent: 7174292 (2007-02-01), Deng et al.
patent: 7181390 (2007-02-01), Droppo et al.
patent: 7200557 (2007-04-01), Droppo et al.
patent: 7206741 (2007-04-01), Deng et al.
patent: 7254536 (2007-08-01), Deng et al.
patent: 2003/0055627 (2003-03-01), Balan et al.
patent: 2003/0055640 (2003-03-01), Burshtein et al.
patent: 2003/0191638 (2003-10-01), Droppo et al.
Wikipedia, “Computational formula for the variance”, One Page.
U.S. Appl. No. 10/152,127, filed Jan. 2006, Droppo et al.
U.S. Appl. No. 10/152,143, filed May 2002, Deng et al.
Droppo, J. et al, “Uncertainty Decoding with Splice for Noise Robust Speech Recognition,” Proceedings of International Conference on Acoustics, Speech and Signal Processing, pp. I-57-I-60, vol. 1, May 2002.
Droppo, J. et al, “Evaluation of the SPLICE Algorithm on the Aurora2 Database,” 7thEuropean Conference on Speech Communication and Technology, Proceedings of Eurospeech 2001, Aalborg, Denmark, Sep. 2001.
Li Deng et al, “A Bayesian Approach to Speech Feature Enhancement using the Dynamic Cepstral Prior,” Proceedings of International Conference on Acoustics, Speech and Signal Processing, pp. I-829-I-832, vol. 1, May 2002.
PCT Search Report for International Application No. PCT/US 03/16032.
U.S. Appl. No. 10/236,042, filed Sep. 5, 2002, Li Deng et al.
“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.
“Recursive Parameter Estimation Using Incomplete Data,” D.M. Titterington, J. J. Royal Stat. Soc., vol. 46(B), 1984, pp. 257-267.
“The Aurora Experimental Framework for the Performance Evaluations of Speech Recognition Systems Under Noisy Conditions,” David Pearce, et al., Proc. ISCA IIRW ASR 2000, Sep. 2000.
“Efficient On-Line Acoustic Environment Estimation for FCDCN in a Continuous Speech Recognition System,” Jasha Droppo, et al., ICASSP, 2001.
“Speech Recognition in Noisy Environments,” Pedro J. Moreno, Ph.D thesis, Carnegie Mellon University, 1996.
“Robust Automatic Speech Recognition With Missing and Unreliable Acoustic Data,” Martin Cooke, Speech Communication, vol. 34, No. 3, pp. 267-285, Jun. 2001.
“Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition,” Brendan J. Frey, et al., Neural Information Processing Systems Conference, 2001, pp. 1165-1121.
“Speech Denoising and Dereverberation Using Probabilistic Models,” Hagai Attias, et al., Advances 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, Sep. 2001.
“Large-Vocabulary Speech Recognition Under Adverse Acoustic Environments,” Li Deng, et al., Proc. ICSLP, vol. 3, 2000, pp. 806-809.
“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.
“Recursive Noise Estimation Using Iterative Stochastic Approximation For Stereo-Based Robust Speech Recognition,” Deng, et al., Proceedings of Automatic Speech Recognition and Understanding 2001.
Office Action (Feb. 16, 2006) and Response (May 16, 2006) from U.S. Appl. No. 10/152,127, filed May 20, 2002.
Office Action (Feb. 14, 2006) and Response (May 12, 2006) from U.S. Appl. No. 10/152,143, filed May 20, 2002.
Office Action (May 11, 2006) and Response (Aug. 11, 2006) from U.S. Appl. No. 10/236,042, filed Sep. 5, 2002.
Deng et al., “Incremental Bayes Learning with Prior Evolution for Tracking Nonstationary Noise Statistics from Noisy Speech Data,” ICASSP '03. Apr. 6-10, 2003, vol. 1, pp. I-672 to I-675.
Droppo et al, “Noise Robust Speech Recognition with a Switching Linear Dynamic Model,” ICASSP '04, May 17-24, 2004, vol. 1, pp. I-953 to I-956.
Deng et al., “Estimating Cepstrum of Speech Under the Presence of Noise Using a Joint Prior of Static and Dynamic Features,” IEEE Transactions on Speech and Audio, May 2004, vol. 12, Issue 3, pp. 218-233.
Acero Alejandro
Deng Li
Droppo James G.
Lerner Martin
Magee Theodore M.
Microsoft Corporation
Westman Champlin & Kelly P.A.
LandOfFree
Method of determining uncertainty associated with acoustic... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method of determining uncertainty associated with acoustic..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method of determining uncertainty associated with acoustic... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3859335