Speech Recognition Using Hidden Markov Models with Polynomial Regression Functions as Nonstationary States

We propose, implement, and evaluate a class of nonstationary-state hidden Markov models (HMM's) having each state associated with a distinct polynomial regression function of time plus white Gaussian noise. The model represents the transitional acoustic trajectories of speech in a parametric manner, and includes the standard stationary-state HMM as a special, degenerated case. We develop an efficient dynamic programming technique which includes the state sojourn time as an optimization variable, in conjunction with a state-dependent orthogonal polynomial regression method, for estimating the model parameters. Experiments on fitting models to speech data and on limited-vocabulary speech recognition demonstrate consistent superiority of these nonstationary-state HMM's over the traditional stationary-state HMM's.