Prediction of Speech Pauses Based on Punctuation Information and Statistical Language Model
QIAN Yi-Li1,2, XUN En-Dong3
1.College of Computer Science, Beijing University of Technology, Beijing 1000222. College of Computer and Information Technology, Shanxi University, Taiyuan 0300063. College of Information Sciences, Beijing Language and Culture University, Beijing 100083
Abstract：Speech pauses are considered as punctuation marks of spoken language. People always insert different pauses at the boundaries of rhythmic phrases when communicating by language. Based on this characteristic, the speech pause of punctuation marks is investigated and the concept of predicting speech pauses using punctuation information is proposed. The punctuation-based and SLM-based methods are introduced to obtain training corpus and predict speech pauses. The influence of training corpus size on the performance of model is discussed. And the performance of punctuation-based corpus and manually-labeled corpus is compared. Experimental results show that the Chinese punctuation supplies valuable information on pause, and the method based on punctuation information can predict the Chinese speech pauses effectively.
钱揖丽，荀恩东. 基于标点信息和统计语言模型的语音停顿预测*[J]. 模式识别与人工智能, 2008, 21(4): 541-545.
QIAN Yi-Li, XUN En-Dong. Prediction of Speech Pauses Based on Punctuation Information and Statistical Language Model. , 2008, 21(4): 541-545.
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2008, Vol. 21 
