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首页> 外文期刊>International Journal of Computer Processing of Oriental Languages >Thai Spelling Recognition Using a Continuous Speech Corpus
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Thai Spelling Recognition Using a Continuous Speech Corpus

机译：使用连续语音语料库的泰语拼写识别

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摘要

Spelling recognition provides alternative input method for computer systems as well as enhances a speech recognizer to cope with incorrectly recognized words and out-of-vocabulary words. This paper presents a general framework of Thai speech recognition enhanced with spelling recognition. Towards the implementation of Thai spelling recognition, Thai alphabets and their spelling methods are analyzed. A method based on hidden Markov models is proposed for constructing a Thai spelling recognition system from an existing continuous speech corpus. To compensate speed difference between spelling utterances and continuous speech utterances, the adjustment of utterance speed is taken into account. Two alternative language models, bigram and digram, are used to investigate the performance of spelling recognition under three different environments: close-type, open-type and mix-type language models. Using the 1.25-times-stretched training utterances under the mix-type language model, the system achieves 87.37% correctness and 87.18% accuracy for bigram, and up to 91.12% correctness and 90.80% accuracy for trigram.
机译：拼写识别为计算机系统提供了替代的输入方法，并增强了语音识别器以应对错误识别的单词和词汇外单词。本文介绍了通过拼写识别增强的泰国语音识别的一般框架。为了实现泰文拼写识别，分析了泰文字母及其拼写方法。提出了一种基于隐马尔可夫模型的方法，用于从现有的连续语音语料库构建泰语拼写识别系统。为了补偿拼写发声和连续语音发声之间的速度差异，考虑了发声速度的调整。两种替代语言模型bigram和digram用于研究三种不同环境下的拼写识别性能：封闭型，开放型和混合型语言模型。使用混合类型语言模型下的1.25倍拉伸训练语音，该系统对bigram可以达到87.37％的正确性和87.18％的准确性，对于trigram可以达到91.12％的正确性和90.80％的准确性。

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