论文信息 - An improved union model for continuous speech recognition with partial duration corruption

An improved union model for continuous speech recognition with partial duration corruption

The probabilistic union model is improved for continuous speech recognition involving partial duration corruption, assuming no knowledge about the corrupting noise. The new developments include: an n-best rescoring strategy for union based continuous speech recognition; a dynamic segmentation algorithm for reducing the number of corrupted segments in the union model; a combination of the union model with conventional noise-reduction techniques to accommodate the mixtures of stationary noise (e.g. car) and random, abrupt noise (e.g. a car horn). The proposed system has been tested for connected-digit recognition, subjected to various types of noise with unknown, time-varying characteristics. The results have shown significant robustness for the new model.

Ji Ming

[1] Richard Lippmann,et al. Using missing feature theory to actively select features for robust speech recognition with interruptions, filtering and noise KN-37 , 1997, EUROSPEECH.

[2] Phil D. Green,et al. Missing data techniques for robust speech recognition , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3] Francis Jack Smith,et al. Union: A new approach for combining sub-band observations for noisy speech recognition , 2001, Speech Commun..

[4] Phil D. Green,et al. Missing data theory, spectral subtraction and signal-to-noise estimation for robust ASR: an integrated study , 1999, EUROSPEECH.

[5] Andrzej Drygajlo,et al. Speaker verification in noisy environments with combined spectral subtraction and missing feature theory , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).