Noisy speech feature estimation on the Aurora2 database using a switching linear dynamic model

Journal of Multimedia

Volumn 2, Issue 2, 2007, Pages 47-52

  Deng, Jianping ^a   Bouchard, Martin ^a   Yeap, Tet Hin ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Hidden Markov model; Speech feature enhancement; Speech recognition; Switching linear dynamic model

Indexed keywords

ADDITIVE NOISE; DYNAMIC MODELS; HIDDEN MARKOV MODELS; MARKOV PROCESSES; SPEECH; SPEECH ENHANCEMENT;

LOG-SPECTRAL DOMAIN; NOISE ENVIRONMENTS; PARAMETRIC MODELING; RECOGNITION ACCURACY; SPEECH FEATURE ENHANCEMENT; STATE TRANSITIONS; SWITCHING LINEAR DYNAMIC MODELS; TIME EVOLUTIONS;

SPEECH RECOGNITION;

EID: 54349096464     PISSN: 17962048     EISSN: None     Source Type: Journal    
DOI: 10.4304/jmm.2.2.47-52     Document Type: Article

References (15)

1. P.J. Moreno, Speech recognition in noisy environments, Ph.D Thesis, Carnegie Mellon University, 1996.
2. B. Raj, R. Singh, and R. Stern "On tracking noise with linear dynamical system models", IEEE ICASSP'04, Vol. 1, pp. I965-I968, May 2004.
3. S. Kim, "IMM-based estimation for slowly evolving environments", IEEE Signal Processing Letters, Vol. 5, pp. 146-149, June 1998.
4. J. Deng, M. Bouchard, and T.H. Yeap, Noise compensation using interacting multiple Kalman filters, InterSpeech 2005, pp. 949-952, Sept. 2005.
5. L. Deng, J. Droppo and A. Acero, "Estimating cepstrum of speech under the presence of noise using a joint prior of static and dynamic features", IEEE Trans. Speech and Audio Processing, Vol. 12, pp. 218-233, May 2004.
6. J. Droppo and A. Acero, "Noise robust speech recognition with a switching linear dynamic model", IEEE ICASSP'04, Vol. 1, pp. I953-I956, May 2004.
7. N.S. Kim, W. Lim, and R. Stern, "Feature compensation based on switching linear dynamic model", IEEE Signal Processing Letters, Vol. 12, pp. 473-476, June 2005.
8. P. Kenny, M. Lenning, and P. Mermelstein, "A linear predictive HMM for vector-valued observations with applications to speech recognition", IEEE Trans. Acoustics, Speech and Signal Processing, Vol. 38, pp. 220-225, Feb. 1990.
9. L.R. Rabiner, "A tutorial on Hidden Markov Models and selected application in speech recognition", Proc. IEEE, Vol. 77, pp. 257-286, Feb. 1989.
10. V. Digalakis, J.R. Rohlicek, and M. Ostendorf, "ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition", IEEE Transactions on Speech and Audio Processing, Vol. 1, No. 4, pp. 431-442, 1993.
11. J. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum press, New York, 1981.
12. Y. B. Shalom and X. R. Li, Estimation and Tracking, Boston Artech house, 1993.
13. S. J. Julier, and J. K. Uhlmann, "A new extension of the Kalman Filter to Nonlinear Systems." Proceedings of AeroSence: The 11th Int. Symp. On Aerospace/Defence Sensing, Simulation and Controls, 1997 (Available at http://citeseer.ist.psu.edu/julier97new.html).
14. E. A. Wan, R. V. D. Merwe, "The unscented Kalman filter for nonlinear estimation", IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium AS-SPCC., pp.153-158, Oct. 2000.
15. H. Hirsch and D. Pearce, "The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions," Proceedings of ISCA ITRW ASR2000 on Automatic Speech recognition: Challenges for the next Millennium, Paris, France, Sept. 2000.