A segmental-feature HMM for continuous speech recognition based on a parametric trajectory model

Abstract

In this paper, we propose a new acoustic model for characterizing segmental features and an algorithm based upon a general framework of hidden Markov models (HMMs). The segmental features are represented as a trajectory of observed vector sequences by a polynomial regression function. To obtain the polynomial trajectory from speech segments, we modify the design matrix to include transitional information for contiguous frames. We also propose methods for estimating the likelihood of a given segment and trajectory parameters. The observation probability of a given segment is represented as the relation between the segment likelihood and the estimation error of the trajectories. The estimation error of a trajectory is considered the weight of the likelihood of a given segment in a state. This weight represents the probability of how well the corresponding trajectory characterizes the segment. The proposed model can be regarded as a generalization of a conventional HMM and a parametric trajectory model. We conducted several experiments to establish the effectiveness of the proposed method and the characteristics of the segmental features. The recognition results on the TIMIT database demonstrate that the performance of segmental-feature HMM (SFHMM) is better than that of a conventional HMM. (C) 2002 Elsevier Science B.V. All rights reserved.