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 by a continuous Gaussian mixture density. In this manner the inherent quantization error introduced by the discrete representation is essentially eliminated. Several issues involved in the training of the continuous density models, and in the implementation of the recognizer are discussed.