Topic segmentation of speech data based on keyword detection

基于关键词检测的语音数据主题分割

基本信息

批准号：
10680415
负责人：
YAMASHITA Yoichi
金额：
$ 2.11万
依托单位：
Ritsumeikan University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 1999
项目状态：
已结题

项目摘要

A method of topic identification is proposed for TV news speech based on the keyword spotting technique. Three thousands of nouns are selected as keywords which contribute to topic identification, based on criterion of mutual information and a length of the word. The topic of news is identified by calculating possibilities of the topics in terms of an acoustic score of the spotted word and a topic probability of the word. Topic identification rate is 66.5 percent assuming that identification is correct if the correct topic is included in the first three places of the result of topic identification.A new method of keyword spotting using prosodic information as well as phonemic information is discussed in order to reduce false alarms keeping high detection rate. Prosodic dissimilarity between a keyword and input speech is measured by DP matching of F0 contours. A total score based on these two measures is used for detecting keywords. The F0 template are stored for each keyword after smoothing based on the F0 model. The introduction of F0 information reduced false alarms by 30% to 50% for the same detection rate for TV news speech.A method of estimating accuracy of word spotting is proposed and it is compared with the estimation based on the length of the word. False alarm counts are estimated by a new measure calculated by simulation of speech recognition for phoneme sequences that the language model generates. The simulation-based measure shows better performance for the estimation of false alarms.

提出了一种基于关键字发现技术的电视新闻演讲的主题识别方法。根据相互信息的标准和单词的长度，选择了三千个名词作为关键字，这些名词有助于主题识别。新闻主题是通过根据斑点单词的声学得分和单词的主题概率来计算主题的可能性来确定的。主题识别率为66.5％，假设识别是正确的，如果在主题识别结果的前三个位置中包含正确的主题，则讨论了使用韵律信息以及音调信息的新方法，以减少虚假警报，以减少保持高检测率的错误警报。关键字和输入语音之间的韵律差异是通过F0轮廓的DP匹配来衡量的。基于这两种措施的总分数用于检测关键字。基于F0模型平滑后，为每个关键字存储F0模板。对于电视新闻语音的相同检测率，F0信息的引入将错误警报降低了30％至50％。提出了一种估计单词斑点准确性的方法，并将其与基于单词长度的估计进行了比较。虚假警报计数是通过通过模拟语言模型生成的音素序列的语音识别计算得出的新测量来估算的。基于仿真的度量显示了估计错误警报的更好的性能。