HMM adaptation techniques in training framework

Abstract

This paper presents an adaptation approach based on the Baum-Welch algorithm method. This method applies the same framework as is are used for training speech recognizers with abundant training data. The Baum-Welch adaptation method is adapted to all the parameters of the hidden Markov models (HMM) with adaptation data. If a large amount of adaptation data is available, these methods could gradually approximate the speaker-dependent ones. The approach is evaluated through the phoneme recognition task on the TIMIT corpus. On the speaker adaptation experiments, up to 91.48% recognition rate is achieved.

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