Sixth European Conference on Speech Communication and Technology
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.
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Bibliographic reference. Hong, Wei-Tyng / Chen, Sin-Horng (1999): "A robust environment-effects suppression training algorithm for adverse Mandarin speech recognition", In EUROSPEECH'99, 2495-2498.