| Budget Amount *help |
¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 1999: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1998: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1997: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Research Abstract |
Spoken language is the most fundamental, fast and convenient method for human to communicate with information processing systems. An automatic speech recognition is the function of speech perception for the information processing system. The purpose of this research is to develop the model construction method for recognition systems with high performance using the genetic algorithm (GA), and to show the effectiveness of the method experimentally.
The hidden Markov models (HMMs) are widely used for automatic speech recognition. However, the HMM has a problem still unresolved, i.e. how to design the optimal structure of the model.
In order to search out the optimal structure of the HMM, we propose in this study the application of the GA which is the model of natural evolution process. In this algorithm, models with higher performance survive and models with lower likelihood die as the generation proceeds, then finally, the globally optimal structure is obtained.
First, we applied the GA to the determination of the discrete HMM's structure for spoken word recognition. As a result of the recognition experiment, it was shown that the structures with higher recognition scores are obtained as the generation proceeds, not only in the case of closed tests but also open tests. The recognition score became higher than that of the Left-Right structure which is the most popular and with high performance, and the effectiveness of the GA was shown. Next, the GA was applied to the continuous HMM, and the effective result was obtained similarly to the discrete HMM. As the revised version of this method, the coding method of word set, the hidden gene method and the crossover and mutation in a state were shown to be effective.
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