| 摘要 |
PROBLEM TO BE SOLVED: To provide a learning method of a hidden Markov model(HMM) capable of achieving a higher recognition rate under noise environments by performing HMM learning from voice data for learning, outputting a noise mixed voice model, removing an estimated noise model, and outputting a non- noise-dependent voice model with noise removed. SOLUTION: A noise mixed voice data 10 for learning are voice data mixed with noise. An HMM learning processing part 11 performs HMM learning based on the noise mixed voice data 10 for learning, and outputs the result to a noise subtraction processing part 15 as a noise mixed voice model 12. A noise estimation part 13 estimates noise from the noise mixed voice data 10 for learning and outputs an estimated noise model 14. The noise subtraction processing part 15 removes the estimated noise model 14 from the noise mixed voice model 12, and outputs a non-noise-dependent voice model 16 with noise removed. As a result, a sturdy voice model can be prepared without being confined to a specific noise at the time of learning.
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