On combining frequency warping and spectral shaping in HMM based speech recognition

Frequency warping approaches to speaker normalization have been proposed and evaluated on various speech recognition tasks. These techniques have been found to significantly improve performance even for speaker independent recognition from short utterances over the telephone network. In maximum likelihood (ML) based model adaptation a linear transformation is estimated and applied to the model parameters in order to increase the likelihood of the input utterance. The purpose of this paper is to demonstrate that significant advantage can be gained by performing frequency warping and ML speaker adaptation in a unified framework. A procedure is described which compensates utterances by simultaneously scaling the frequency axis and reshaping the spectral energy contour. This procedure is shown to reduce the error rate in a telephone based connected digit recognition task by 30-40%