Boosting Thai Syllable Speech Recognition Using Acoustic Models Combination

S. Tangwongsan, R. Phoophuangpairoj
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引用次数: 10

Abstract

In this paper, a highly effective system for Thai speech recognition is proposed. The speech recognizer for so-called speaker-independent is created by using Continuous Density Hidden Markov Model (CDHMM). In the acoustic level, the models trained for both speaker genders, and for each separate gender are investigated and tested in terms of accuracy. Experimental evaluation shows that with the acoustic models combination, the accuracy could be improved considerably in the acoustic level. The acoustic combination can support spoken utterances from both genders and still provide the high accuracy simultaneously. Interestingly, when using the acoustic models combination, the syllable accuracy of 89.84% is achieved with 4.53% improvement over using the conventional acoustic models trained for both genders.
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利用声学模型组合增强泰语音节语音识别
本文提出了一种高效的泰语语音识别系统。利用连续密度隐马尔可夫模型(CDHMM)建立了独立于说话人的语音识别器。在声学层面,针对说话者性别和每个单独性别训练的模型在准确性方面进行了调查和测试。实验结果表明,声学模型组合后,在声学水平上精度有较大提高。这种声学组合既能支持男女语音,又能同时提供高精度。有趣的是,当使用声学模型组合时,音节准确率达到89.84%,比使用传统的男女声学模型提高4.53%。
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