Noisy speech recognition using robust inversion of hidden Markov models

Abstract

The hidden Markov model (HMM) inversion algorithm is proposed and applied to robust speech recognition for general types of mismatched conditions. The Baum-Welch HMM inversion algorithm is a dual procedure to the Baum-Welch HMM reestimation algorithm, which is the most widely used speech recognition technique. The forward training of an HMM, based on the Baum-Welch reestimation, finds the model parameters /spl lambda/ that optimize some criterion, usually maximum likelihood (ML), with given speech inputs s. On the other hand, the inversion of a HMM finds speech inputs s that optimize some criterion with given model parameters /spl lambda/. The performance of the proposed HMM inversion, in conjunction with HMM reestimation, for robust speech recognition under additive noise corruption and microphone mismatch conditions is favorably compared with other noisy speech recognition techniques, such as the projection-based first-order cepstrum normalization (FOCN) and the robust minimax (MINIMAX) classification techniques.