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首页> 外文期刊>British Journal of Applied Science and Technology >Comparison of Syllable and Phoneme Modelling of Agglutinative Tamil Isolated Words in Speech Recognition
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Comparison of Syllable and Phoneme Modelling of Agglutinative Tamil Isolated Words in Speech Recognition

机译：语音识别中泰米尔语孤立单词的音节和音素建模比较

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Aim: In this paper, the emphasis was on improving the automatic speech recognition of Tamil speech by applying syllable and phoneme as a sub-word unit. Agglutinative complex words in Tamil are described by showing their element in the building of the sub-word such as the syllable and the phonemes. This present study used the Hidden Markov Model (HMM) based speech recognition system that was created using CMU Sphinx speech recognition toolkit. An effective consonant-vowel six-segment (CVS-6) algorithm was designed to syllabification of the Tamil isolated words and experimentally investigated its speech recognition accuracy. The database used in this study was designed using a maximum of 160 isolated words, representing 430 syllables and 216 unique syllables. Results: Through the experiment, the syllable-based model achieved a mean recognition rate of 93.84 (standard deviation, 5.02) compared to 91.37 (standard deviation, 6.26) achieved by a phoneme-based model. Conclusion: It was concluded from this research that the syllable-based model using the CVS-6 algorithm is a good choice and can be used in the development of sub-word modelling of isolated words, which is an effective sub-word modelling of medium and large vocabulary ASR Tamil language.
机译：目的：在本文中，重点是通过将音节和音素作为子词单元来改进泰米尔语语音的自动语音识别。泰米尔语中的拼写复杂单词通过在子单词（如音节和音素）的构建中显示其元素来描述。本研究使用基于隐马尔可夫模型（HMM）的语音识别系统，该系统是使用CMU Sphinx语音识别工具包创建的。设计了一种有效的辅音元音六段（CVS-6）算法来对泰米尔语孤立词进行音节化，并通过实验研究了其语音识别准确性。本研究中使用的数据库最多使用160个孤立的单词进行设计，分别代表430个音节和216个唯一音节。结果：通过实验，基于音节的模型的平均识别率达到93.84（标准差，5.02），而基于音素模型的平均识别率达到91.37（标准差，6.26）。结论：从这项研究得出的结论是，使用CVS-6算法的基于音节的模型是一个不错的选择，可用于开发孤立词的子词建模，这是一种有效的中等子词建模以及大量的ASR泰米尔语词汇。

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