Using Gaussian mixture modeling in speech recognition

Abstract

The paper describes a speaker-independent isolated word recognition system which uses a well known technique, the combination of vector quantization with hidden Markov modeling. The conventional vector quantization algorithm is substituted by a statistical clustering algorithm, the expectation-maximization algorithm, in this system. Based on the investigation of the data space, the phonemes were manually extracted from the training data and were used to generate the Gaussians in a code book in which each code word is a Gaussian rather than a centroid vector of the data class. Word-based hidden Markov modeling was then performed. Two English isolated digits data bases were investigated and the 12 Mel-spaced filter bank coefficients employed as the input feature. Compared with the conventional discrete HMM, the present system obtained a significant improvement of recognition accuracy.<>