Recursive Estimation of Nonstationary Noise Using Iterative Stochastic Approximation for Robust Speech Recognition

IEEE Transactions on Speech and Audio Processing

Volumn 11, Issue 6, 2003, Pages 568-580

  Deng, Li ^a   Droppo, Jasha ^a   Acero, Alex ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; APPROXIMATION THEORY; ITERATIVE METHODS; MATRIX ALGEBRA; RANDOM PROCESSES; SIGNAL PROCESSING;

NOISE ESTIMATION METHOD; NONSTATIONARY ACOUSTIC NOISE;

SPEECH RECOGNITION;

EID: 0347968277     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSA.2003.818076     Document Type: Article

References (20)

1. A. Acero, L. Deng, T. Kristjansson, and J. Zhang, "HMM adaptation using vector Taylor series for noisy speech recognition," in Proc. ICSLP, vol. 3, 2000, pp. 869-872.
2. M. Afify and O. Siohan, "Sequential noise estimation with optimal forgetting for robust speech recognition," in Proc. ICASSP, vol. 1, 2001, pp. 229-232.
3. A. Benveniste, M. Metivier, and P. Priouret, Adaptive Algorithms and Stochastic Approximations - Applications of Mathematics. New York: Springer, 1990, vol. 22.
4. L. Deng, A. Acero, M. Plumpe, and X. D. Huang, "Large-vocabulary speech recognition under adverse acoustic environments," in Proc. ICSLP, vol. 3, 2000, pp. 806-809.
5. L. Deng, A. Acero, L. Jiang, J. Droppo, and X. D. Huang, "High-performance robust speech recognition using stereo training data," in Proc. ICASSP, vol. 1, 2001, pp. 301-304.
6. L. Deng and J. Ma, "Spontaneous speech recognition using a statistical coarticulatory model for the hidden vocal-tract-resonance dynamics," J. Acoust. Soc. Amer., vol. 108, no. 6, pp. 3036-3048, Dec. 2000.
7. J. Droppo, L. Deng, and A. Acero, "Efficient on-line acoustic environment estimation for FCDCN in a continuous speech recognition system," in Proc. ICASSP, vol. 1, April 2001, pp. 209-212.
8. _, "Evaluation of the SPLICE algorithm on the AURORA2 database," in Proc. Eurospeech, vol. 1, Sept. 2001, pp. 217-220.
9. Y. Ephraim, "Statistical-model-based speech enhancement systems," Proc. IEEE, vol. 80, pp. 1526-1555, Oct. 1992.
10. L. Frenkel and M. Feder, "Recursive expectation-maximization (EM) algorithms for time-varying parameters with applications to multiple target tracking," in IEEE Trans. Signal Processing, vol. 42, 1999, pp. 306-320.
11. B. Prey, L. Deng, A. Acero, and T. Kristjansson, "ALGONQUIN: Iterating Laplace's method to remove multiple types of acoustic distortion for robust speech recognition," in Proc. Eurospeech, vol. 2, Sept. 2001, pp. 901-904.
12. H. Hirsch and D. Pearce, "The AURORA experimental framework for the performance evaluations of speech recognition systems under noisy conditions," in Proc. ISCA ITRW ASR2000 on Automatic Speech Recognition: Challenges for the Next Millennium, Paris, France, Sept. 2000.
13. A. H. Jazwinski, Stochastic Processes and Filtering Theory. New York: Academic, 1970.
14. N. S. Kim, "Nonstationary environment compensation based on sequential estimation," IEEE Signal Processing Lett., vol. 5, pp. 57-60, 1998.
15. V. Krishnamurthy and J. B. Moore, "Online estimation of hidden markov model parameters based on the Kullback-Leibler information mature," IEEE Trans. Signal Processing, vol. 41, pp. 2557-2573, 1993.
16. J. Mendel, Lessons in Estimation Theory for Signal Processing, Communications and Control (Lesson 24). Englewood Cliffs, NJ: Prentice-Hall, 1995.
17. P. Moreno, B. Raj, and R. Stern, "A vector taylor series approach for environment-independent speech recognition," in Proc. ICASSP, vol. 1, 1996, pp. 733-736.
18. D. M. Titterington, "Recursive parameter estimation using incomplete data," J. R. Statist. Soc. B, vol. 46, pp. 257-267, 1984.
19. O. Viikki, Speech Commun. (Special Issue on Noise Robust ASR), O. Viikki, Ed., 2001, vol. 34.
20. E. Weinstein, M. Feder, and A. Oppenheim, "Sequential algorithms for parameter estimation based on Kullback-Leibler information measure," IEEE Trans. Signal Processing, vol. 38, pp. 1652-1654, 1990.