| 摘要 |
PROBLEM TO BE SOLVED: To improve the precision in estimating movement vectors and to enhance the voice recognition rate by selecting prescribed higher-order plural vectors in which the value of the distance between an object vector that it to be processed and a vicinity vector is small. SOLUTION: A speaker adaptive control section 31 adaptively learns an initial speaker model 30 which includes speaker's cluster models using speaker adaptive learning data 32, which are the sentence uttering text data, for example, converts the model into an unspecified speaker phoneme model of the phoneme HMM, stores the model in the memory of a hidden Markov network (an HM network) and performs voice recognition based on the network 11. Specifically, the section 31 successively executes the computational process of the movement vectors, the interpolation process of the movement vectors, the smoothing process of the movement vectors and the learning process, in which speaker-adapting is conducted, employing the processed movement vectors. During the interpolation and flattering/smoothing processes of the movement vectors, the selection of vicinity vectors is conducted using the tree construction of the state dividing process by a known sequential state dividing method. |
