Importance

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Abstract

The invention relates to speech recognition based on HMM, in which speech recognition is performed by performing vector quantization and obtaining an output probability by table reference, and the amount of computation and use of memory area are minimized while achieving a high ability of recognition. Exemplary codebooks used for vector quantization can be provided as follows: if phonemes are used as subwords, codebooks for respective phonemes, such that a codebook CB1 is a codebook for a phoneme /a/ and a codebook CB2 is a codebook for a phoneme /i/, and these codebooks are associated with respective phoneme HMMs. When a feature vector obtained by speech analysis is vector quantized based on, for example, the codebook CB1 and a code (label) is output, tables for respective states of the phoneme HMM associated with the codebook CB1 are each referred to in order to obtain state output probabilities corresponding to the label, and speech recognition is performed using the state output probabilities as a parameter.

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First Claim