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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. The resulting recognizer was tested on a vocabulary of the 10 digits across a wide range of talkers and test conditions, and shown to have an error rate at least comparable to that of the best template recognizers and significantly lower than that of the discrete symbol hidden Markov model system. Several issues involved in the training of the continuous density models and in the implementation of the recognizer are discussed.