Recognition of Isolated Digits Using Hidden Markov Models With Continuous Mixture Densities

In this paper we extend previous work on isolated-word recognition based on hidden Markov models by replacing the discrete symbol representation of the speech signal with a continuous Gaussian mixture density. In this manner the inherent quantization error introduced by the discrete representation is essentially eliminated. The resulting recognizer was tested on a vocabulary of the ten digits across a wide range of talkers and test conditions and shown to have an error rate comparable to that of the best template recognizers and significantly lower than that of the discrete symbol hidden Markov model system. We discuss several issues involved in the training of the continuous density models and in the implementation of the recognizer.