| 摘要 |
Provided is a method and system for high-speed speech recognition. On the basis of a continuous density hidden Markov model (CDHMM) using a Gaussian mixture model (GMM) for an observation probability, the method and system add only K Gaussian components highly contributing to a state-specific observation probability for an input feature vector and calculate the state-specific observation probability. Thus, in the aspect of the recognition ratio, the degree of approximation of a state-specific observation probability increases, thereby minimizing deterioration of speech recognition performance. In addition, in the aspect of the amount of computation, the number of addition operations required for computing an observation probability is reduced, in comparison with conventional speech recognition that adds all Gaussian probabilities of an input feature vector and uses it for a state-specific observation probability, thereby reducing the total amount of computation required for speech recognition.
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