Noise reduction using correction vectors based on dynamic...

Details Noise reduction using correction vectors based on dynamic... Noise reduction using correction vectors based on dynamic...

2007-02-20

2007-02-20

{hacek over (S)}mits, Talivaldis Ivars (Department: 2626)

Data processing: speech signal processing, linguistics, language

Speech signal processing

For storage or transmission

C704S224000

Reexamination Certificate

active

11189974

ABSTRACT:
A method and apparatus are provided for reducing noise in a signal. Under one aspect of the invention, a correction vector is selected based on a noisy feature vector that represents a noisy signal. The selected correction vector incorporates dynamic aspects of pattern signals. The selected correction vector is then added to the noisy feature vector to produce a cleaned feature vector. In other aspects of the invention, a noise value is produced from an estimate of the noise in a noisy signal. The noise value is subtracted from a value representing a portion of the noisy signal to produce a noise-normalized value. The noise-normalized value is used to select a correction value that is added to the noise-normalized value to produce a cleaned noise-normalized value. The noise value is then added to the cleaned noise-normalized value to produce a cleaned value representing a portion of a cleaned signal.

REFERENCES:
patent: 4718094 (1988-01-01), Bahl et al.
patent: 4918735 (1990-04-01), Morito et al.
patent: 4980917 (1990-12-01), Hutchins
patent: 5012519 (1991-04-01), Adlersberg et al.
patent: 5390278 (1995-02-01), Gupta et al.
patent: 5583968 (1996-12-01), Trompf
patent: 5590242 (1996-12-01), Juang et al.
patent: 5604839 (1997-02-01), Acero et al.
patent: 5758022 (1998-05-01), Trompf et al.
patent: 5924065 (1999-07-01), Eberman et al.
patent: 5950157 (1999-09-01), Heck et al.
patent: 6026359 (2000-02-01), Yamaguchi et al.
patent: 6067517 (2000-05-01), Bahl et al.
patent: 6092045 (2000-07-01), Stubley et al.
patent: 6202047 (2001-03-01), Ephraim et al.
patent: 6292775 (2001-09-01), Holmes
patent: 6301561 (2001-10-01), Saul
patent: 6401064 (2002-06-01), Saul
patent: 6446038 (2002-09-01), Bayya et al.
patent: 6490555 (2002-12-01), Yegnanarayanan et al.
patent: 6691091 (2004-02-01), Cerisara et al.
patent: 6778954 (2004-08-01), Kim et al.
patent: 0 301 199 (1989-02-01), None
patent: 0 694 906 (1996-01-01), None
Sameti, H. HMM-based strategies for enhancement of speech signals embedded in nonstationary noise, Sep. 1998, Speech and Audio Processing, IEEE Transaction on, vol. 6, Issue 5, pp. 445-455.
U.S. Appl. No. 10/116,792, filed Apr. 5, 2002, Li Deng et al.
U.S. Appl. No. 09/688,764, filed Oct. 16, 2000, Li Deng et al.
U.S. Appl. No. 09/688,950, filed Oct. 16, 2000, 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.
“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.
“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,” Moreno, P., Department of Electrical and Computer Engineering, Carnegie Mellon University, pp. 1-130 (Apr. 22, 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.
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.
Sameti, H. “HMM-based strategies for enhancement of speech signal embedded in nonstationary noise,” Sep. 1998, Speech and Audio Processing, IEEE Transaction on, vol. 6, Issue 5, pp. 445-455.
Office Action dated Jul. 13, 2005; Response dated Jul. 26, 2005 from U.S. Appl. No. 10/117,142, filed Apr. 5, 2002.
Ephraim, Yariv, “On the Application of Hidden Markov Models for Enhancing Noisy Speech,” IEEE ICASSP vol. 1, conf. 13, pp. 533-536, 1988.
Moreno, Pedro J., “Multivariate-Gaussian-Based Cepstral Normalization for Robust Speech Recognition,” Proceedings of ICASSP, p. 137-140, 1995.
Office Action (Jan. 30, 2006) and Response (Nov. 16, 2005; Nov. 22, 2005; Feb. 3, 2006) from U.S. Appl. No. 10/117,142, filed Apr. 5, 2002.
Zhao, “Frequency-Domain Maximum Likelihood Estimation for Automatic Speech Recognition in Additive and Convolutive Noise,” IEEE Transactions on Speech and Audio Processing, vol. 8, No. 3, May 2000.
Neumeyer, L. and Weintraub, M. “Probabilistic Optimum Filtering For Robust Speech Recognition,” Acoustics, Spe

Affiliated with

Also associated with

Rating

Noise reduction using correction vectors based on dynamic... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Noise reduction using correction vectors based on dynamic..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Noise reduction using correction vectors based on dynamic... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3883591