Study on Automatic Speech Recognition with the Hidden Markov Model Using the Genetic Algorithm

口语是人类与信息处理系统进行交流的最基本、最快捷、最方便的方式。自动语音识别是信息处理系统的语音感知功能。提出了一种基于遗传算法(GA)的高性能识别系统的建模方法，并通过实验验证了该方法的有效性。然而，隐马尔可夫模型仍有一个尚未解决的问题，即如何设计模型的最优结构，为了搜索出隐马尔可夫模型的最优结构，本文提出了将遗传算法作为自然进化过程模型的应用。首先，将遗传算法应用于语音识别中离散隐马尔可夫模型结构的确定。识别实验的结果表明，无论是在封闭测试的情况下，还是在开放测试的情况下，随着生成的进行，都可以获得具有较高识别分数的结构。实验结果表明，该算法的识别率高于最常用且性能较高的左右结构，说明了遗传算法的有效性。然后，将遗传算法应用于连续隐马尔可夫模型，得到了与离散隐马尔可夫模型类似的有效结果。作为该方法的修正版本，词集编码法、隐含基因法和状态交叉变异方法被证明是有效的。

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.