Using Gaussian mixture modeling in speech recognition

tive way to improve the performance of recognizers. This paper describe 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 ExpectationMaximization 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 Gaussiaus in a code book in which each code word is a Gaussian rather than a centroid vector of the da ta class. The word based hidden Markov modeling then was performed. Two English isolated digits data base were investigated and the 12 Mel-spaced filter bank coefficients was employed as the input feature. Comparing the conventional discrete HMM, our system obtained significant improvement of recognition accuracy.