| 摘要 |
PURPOSE:To improve the accuracy of recognition by setting the degree of contribution at every identification symbol in the case of generating a symbol model and calculating likelihood based on a contribution degree information dictionary at every identification symbol of an acoustic feature parameter. CONSTITUTION:An input sound 1 is converted to a sound feature sequence 3 in a sound analysis part 2. In the case of generating the symbol model, the input sound is inputted to a symbol model generation part 6 as a feature sequence 4 for learning and the symbol model 7 is generated. At this time, a contribution degree setting part 16 by symbols sets a feature contribution degree 17 for symbol model generation according to the contribution degree information dictionary 15. Next, a recognition object model generation part 8 generates a recognition object model 10 by linking the symbol models while referring to a recognition object dictionary 9. In a recognition processing, the sequence 3 is inputted to a likelihood calculation part 11 and the likelihood 12 is calculated based on a feature contribution degree 18 for the likelihood calculation from the setting part 16. A sellective part 13 selects and outputs the recognition object model having the highest likelihood. Thus, the accuracy of recognition is improved while considering the difference of an effect to recognition performance between the feature parameters concerning the respective identification symbols. |
