Speech coding apparatus having acoustic prototype vectors generated by tying to elementary models and clustering around reference vectors

Abstract

A speech coding apparatus in which measured acoustic feature vectors are each represented by the best matched prototype vector. The prototype vectors are generated by storing a model of a training script comprising a series of elementary models. The value of at least one feature of a training utterance of the training script is measured over each of a series of successive time intervals to produce a series of training feature vectors. A first set of training feature vectors corresponding to a first elementary model in the training script is identified. The feature value of each training feature vector signal in the first set is compared to the parameter value of a first reference vector signal to obtain a first closeness score, and is compared to the parameter value of a second reference vector to obtain a second closeness score for each training feature vector. For each training feature vector in the first set, the first closeness score is compared with the second closeness score to obtain a reference match score. A first subset contains those training feature vectors in the first set having reference match scores better than a threshold Q, and a second subset contains those having reference match scores less than the threshold Q. One or more partition values are generated for a first prototype vector frown the first subset of training feature vectors, and one or more additional partition values are generated for the first prototype vector from the second subset of training feature vectors.