We extend the nonstationary-state or trended hidden Markov model (HMM) from the previous single-trend formulation (Deng, 1992; Deng et al., 1994) to the current mixture-trended one. This extension is motivated by the observation of wide variations in the trajectories of the acoustic data in fluent, speaker-independent speech associated with a fixed underlying linguistic unit. It is also motivated by potential use of mixtures of trend functions to characterize heterogeneous time-varying data generated from distinctive sources such as the speech signals collected from different microphones or from different telephone channels. We show how HMMs with mixtures of trend functions can be implemented simply in the already well-established single-trend HMM framework via the device of expanding each state into a set of parallel states. Details of a maximum-likelihood-based (ML-based) algorithm are given for estimating state-dependent mixture trajectory parameters in the model. Experimental results on the task of classifying speaker-independent vowels excised from the TIMIT data base demonstrate consistent performance improvement using phonemic mixture-trended HMMs over their single-trend counterpart.

Proposes, implements, and evaluates a class of nonstationary-state hidden Markov models (HMMs) 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. The authors 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 HMMs over the traditional stationary-state HMMs.>