Improvements to VTS feature enhancement

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2012, Pages 4677-4680

  Li, Jinyu ^a   Seltzer, Michael L ^a   Gong, Yifan ^a  

^a MICROSOFT   (United States)

Author keywords

feature enhancement; model adaptation; robust ASR; VTS

Indexed keywords

ADAPTIVE TRAINING; COMPUTATIONAL COSTS; DISTORTION PARAMETERS; ENVIRONMENTAL NOISE; FEATURE ENHANCEMENT; HMM MODELS; MODEL ADAPTATION; REAL-WORLD TASK; ROBUST ASR; RUNTIME COSTS; RUNTIMES; SPEECH RECOGNIZER; SPEECH SIGNALS; VECTOR TAYLOR SERIES; VTS;

SIGNAL PROCESSING;

SPEECH RECOGNITION;

EID: 84867584623     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2012.6288962     Document Type: Conference Paper

References (12)

1. P. Moreno, Speech Recognition in Noisy Environments. PhD. Thesis, Carnegie Mellon University, 1996.
2. M. J. F. Gales, Model-Based Techniques for Noise Robust Speech Recognition, PhD. Thesis, Cambridge University, 1995.
3. A. Acero, et al., "HMM adaptation using vector Taylor series for noisy speech recognition," in Proc. ICSLP, Vol.3, pp. 869-872, 2000.
4. H. Liao and M. J. F. Gales, "Joint uncertainty decoding for robust large vocabulary speech recognition, Tech. Rep. CUED/TR552, University of Cambridge, 2006.
5. J. Li, et al., "A unified framework of HMM adaptation with joint compensation of additive and convolutive distortions," Computer Speech and Language, vol. 23, pp. 389-405, 2009.
6. V. Stouten, et al., "Robust speech recognition using model-based feature enhancement," in Proc. European Conference on Speech Communication and Technology, pp. 17-20, 2003.
7. J. Droppo, L. Deng, and A. Acero, "A comparison of three non-linear observation models for noisy speech features," in Proc. Eurospeech, 2003.
8. O. Kalinli, M. L. Seltzer, and A. Acero, "Noise adaptive training using a vector Taylor series approach for robust automatic speech recognition," in Proc. ICASSP, 2009.
9. V. Stouten, Robust Automatic Speech Recognition in Time-varying Environments, Ph.D. Thesis, K. U. Leuven, Leuven 2006.
10. H. G. Hirsch and D. Pearce, "The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proc. ISCA ITRW ASR, 2000.
11. A. Moreno, et al., "Speechdat-car: a large speech database for automotive environments," in Proc. LREC, 2000.
12. J. Li, et al., "Unscented transform with online distortion estimation for HMM adaptation," in Proc. Interspeech, 2010.