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Abstract

Each word to be recognized is represented by gender-specific hidden Markov models that are stored in a ROM 6 along with output probability functions and preset transition probabilities. A speech recognizer 4 determines an occurrence probability of a feature parameter sequence detected by a feature value detector 3 using the hidden Markov models. The speech recognizer 4 determines the occurrence probability by giving each word a state sequence of one hidden Markov model common to the gender-specific hidden Markov models, multiplying each preset pair of an output probability function value and a transition probability together among the output probability functions and transition probabilities stored in the ROM 6, selecting the largest product as the probability of each state of the common hidden Markov model, determining the occurrence probability based on the selected product, and recognizing the input speech based on the occurrence probability thus determined.

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