Abstract
At the time of the speaker adaptation, first feature vector generation sections ( 7, 8, 9 ) generate a feature vector series [C i, M ] from which the additive noise and multiplicative noise are removed. A second feature vector generation section ( 12 ) generates a feature vector series [S i, M ] including the features of the additive noise and multiplicative noise. A path search section ( 10 ) conducts a path search by comparing the feature vector series [C i, m ] to the standard vector [a n, m ] of the standard voice HMM ( 300 ). When the speaker adaptation section ( 11 ) conducts correlation operation on an average feature vector [S^ n, m ] of the standard vector [a n, m ] corresponding to the path search result Dv and the feature vector series [S i, m ], the adaptive vector [x n, m ] is generated. The adaptive vector [x n, m ] updates the feature vector of the speaker adaptive acoustic model ( 400 ) used for the speech recognition.
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