In this paper, a new robust training algorithm for the generation of a set of bias-removed, noise-suppressed reference speech HMM models directly from a training database collected in adverse environment suffering with both convolutional channel bias and additive noise is proposed. Its main idea is to incorporate a signal bias-compensation operation and a PMC noise-compensation operation into its iterative training process in order to make the resulting speech HMM models more suitable to the given robust speech recognition method using the same signal bias-compensation and PMC noise-compensation operations in the recognition process. Experimental results showed that the speech HMM models it generated outperformed both the clean-speech HMM models and those generated by the conventional k-means algorithm for two adverse Mandarin speech recognition tasks. So it is a promising robust training algorithm.
Cite as: Hong, W.-T., Chen, S.-H. (1999) A robust environment-effects suppression training algorithm for adverse Mandarin speech recognition. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 2495-2498, doi: 10.21437/Eurospeech.1999-545
@inproceedings{hong99_eurospeech,
author={Wei-Tyng Hong and Sin-Horng Chen},
title={{A robust environment-effects suppression training algorithm for adverse Mandarin speech recognition}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={2495--2498},
doi={10.21437/Eurospeech.1999-545}
}