摘要： 根据藏语的特点，提出藏语拉萨话大词表连续语音识别声学模型，利用高层次的藏语语言知识减少模式匹配的模糊性。以音素和声韵母为声学建模单元，在HTK平台上建立上下文相关的连续隐马尔可夫声学模型，以实现藏语拉萨话特定人大词表连续语音识别。实验结果表明，在最优情况下，该模型词错误率只有7.8%。

关键词: 藏语, 拉萨话, 连续语音识别, 隐马尔可夫模型, HTK工具, 声学模型

Abstract: The characteristics of Tibetan are analyzed in this paper. The framework of auto speech recognition of Lhasa dialect is designed. Several feasible units for acoustic models are analyzed. Contextual continuous Hidden Markov Model(HMM) models based on phonemes and semi-syllables are established and trained on Hidden Markov Model Toolkit(HTK) platform respectively and large-vocabulary continuous speech recognition of Lhasa Tibetan is implemented. Experimental results show that Word Error Rate(WER) is 7.8% in the best case.

Key words: Tibetan, Lhasa, continuous speech recognition, Hidden Markov Model(HMM), Hidden Markov Model Toolkit(HTK), acoustic model

中图分类号: 

• TP391