A linear predictive HMM for vector-valued observations with applications to speech recognition

摘要

The authors describe a new type of Markov model developed to account for the correlations between successive frames of a speech signal. The idea is to treat the sequence of frames as a nonstationary autoregressive process whose parameters are controlled by a hidden Markov chain. It is shown that this type of model performs better than the standard multivariate Gaussian HMM (hidden Markov model) when it is incorporated into a large-vocabulary isolated-word recognizer.