摘要
语音停顿被认为是有声语言的标点符号.在语言交流中,说话人会在韵律短语的边界处插入长短不同的停顿.利用这一性质,在调查标点符号停顿作用的基础上,提出基于标点信息预测语音停顿的思想,阐述基于标点和统计模型的训练语料自动获取以及语音停顿预测方法,讨论训练语料规模对模型性能的影响,并比较基于标点信息的自动获取语料与人工标注语料的性能.实验结果显示,汉语的标点提供有价值的停顿信息,基于汉语标点信息能够有效预测语音停顿.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第4期541-545,共5页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基会资助项目(No.60572159
60573184
60473139)
关键词
标点符号
语音停顿
统计语言模型
语料获取
Punctuation Marks, Speech Pause, Statistic Language Model, Corpus Obtaining
