Explicit modeling of coarticulation in a statistical speech recognizer

This paper presents a new statistical speech model in which coarticulation is modeled explicitly. Unlike HMMs, in which the current state depends only on the previous state and the current observation, the proposed model supports dependence on the previous and next states and on the previous and current observations. The degree of coarticulation between adjacent phones is modeled parametrically, and can be adjusted according to a parameter representing the speaking rate. The model also incorporates a parameter that represents a frame-by-frame measure of confidence in the speech. We present two methods for solving the system parameters: one based on the K-means method, and a novel method based on explicitly minimizing a measure of the segmentation error. A new, efficient forward algorithm and the use of top candidates in the search greatly reduce the computational complexity. In evaluation on the TIMIT data base, we achieve a phone recognition rate of 77.1%.