Although as the most popular model for speech recognition

HMM takes no account of the dynamics of the speech trajectory

since it assumes the outputs of a state to be independent and identically distributed.In this paper

based on a more flexible statistical framework for speech description-the generalized DDBHMM

a particular polynomial-fitting speech-trajectory model is proposed with new algorithms for training and recognition.It describes the real characteristics of speech more reasonably.With the effective path-pruning algorithm additionally proposed

it becomes a practicable model.Experiments on Chinese large-vocabulary speaker-independent continuous speech recognition showed that with this path-pruned polynomial-fitting speech-trajectory model

the recognition performance is improved distinctively at relatively low computational cost.