On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition

Abstract

In this paper we present an approach to speaker-independent, isolated word recognition in which the well-known techniques of vector quantization and hidden Markov modeling are combined with a linear predictive coding analysis front end. This is done in the framework of a standard statistical pattern recognition model. Both the vector quantizer and the hidden Markov models need to be trained for the vocabulary being recognized. Such training results in a distinct hidden Markov model for each word of the vocabulary. Classification consists of computing the probability of generating the test word with each word model and choosing the word model that gives the highest probability. There are several factors, in both the vector quantizer and the hidden Markov modeling, that affect the performance of the overall word recognition system, including the size of the vector quantizer, the structure of the hidden Markov model, the ways of handling insufficient training data, etc. The effects, on recognition accuracy, of many of these factors are discussed in this paper. The entire recognizer (training and testing) has been evaluated on a 10-word digits vocabulary. For training, a set of 100 talkers spoke each of the digits one time. For testing, an independent set of 100 tokens of each of the digits was obtained. The overall recognition accuracy was found to be 96.5 percent for the 100-talker test set. These results are comparable to those obtained in earlier work, using a dynamic time-warping recognition algorithm with multiple templates per digit. It is also shown that the computation and storage requirements of the new recognizer were an order of magnitude less than that required for a conventional pattern recognition system using linear prediction with dynamic time warping.