Factor analysis of acoustic features for streamed hidden Markov modeling

This paper presents a new streamed hidden Markov model (HMM) framework for speech recognition. The factor analysis (FA) is performed to discover the common factors of acoustic features. The streaming regularities are governed by the correlation between features, which is inherent in common factors. Those features corresponding to the same factor are generated by identical HMM state. Accordingly, we use multiple Markov chains to represent the variation trends in cepstral features. We develop a FA streamed HMM (FASHMM) and go beyond the conventional HMM assuming that all features at a speech frame conduct the same state emission. This streamed HMM is more delicate than the factorial HMM where the streaming was empirically determined. We also exploit a new decoding algorithm for FASHMM speech recognition. In this manner, we fulfill the flexible Markov chains for an input sequence of multivariate Gaussian mixture observations. In the experiments, the proposed method can reduce word error rate by 36% at most.