Speaker independent isolated digit recognition using hidden Markov models

Abstract

A method for speaker independent isolated digit recognition based on modeling entire words as discrete probabilistic functions of a Markov process is described. Training is a three‐part process comprising conventional methods of linear prediction analysis and vector quantization of the LPCs followed by an algorithm [L. E. Baum, Inequalities 3, 1–8 (1972)] for estimating the parameters of a hidden Markov process. Recognition utilizes linear prediction and vector quantization steps prior to maximum likelihood classification based on the Viterbi algorithm [A. J. Viterbi, IEEE Trans. Inf. Theo. IT‐13, 260–269 (1967)]. After training based on a 1000‐token set, recognition experiments were conducted on a separate 1000‐token test set obtained from 100 new talkers. In this test a 3.5% error rate was observed which is comparable to that measured in an identical test of an LPC/DTW system [L. R. Rabiner et al., IEEE Trans. Acoust. Speech Signal Process. ASSP‐37, 336–349 (1979)]. The computational demand for recognition under the new system is reduced by a factor of approximately ten in both time and memory compared to that of the LPC/DTW system. Issues of selection of model structures, averaging techniques to obtain model stability, and methods of compensating for finite training set size are also discussed.