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Abstract

The acoustic model generating method for speech recognition enables a high representation effect on the basis of the minimum possible model parameters. In an initial model having a smaller number of signal sources, the acoustic model for speech recognition is generated by selecting the splitting processing or the merging processing for the signal sources successively and repeatedly. The merging processing is executed prior to the splitting processing. In the merging processing, when the merged result is not appropriate, the splitting processing is executed for the model obtained before merging processing (without use of the merged result). Further, the splitting processing includes three methods at the same time, as (1) a method of splitting the signal source into two and reconstructing a shared structure between a plurality of states having common signal sources to be split, (2) a method of splitting one state into two states corresponding to different phoneme context categories in phoneme context direction, (3) a method of splitting one state into two states corresponding to different speech sections in time direction. One of the three methods is selected by obtaining three pieces of maximum likelihood for the three splitting steps and judging which one is the biggest to select the splitting step for which the biggest likelihood is obtained.

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