Topic segmentation of speech data based on keyword detection

提出了一种基于关键词识别技术的电视新闻演讲主题识别方法。根据互信息标准和单词长度，选取3000个名词作为主题识别的关键词。新闻的主题是通过计算主题的可能性来确定的，根据被标记单词的声学分数和单词的主题概率。假设题目识别结果的前三位包含正确的题目，那么题目识别率为66.5%。本文讨论了一种结合韵律信息和音位信息的关键字识别方法，以减少误报，保持较高的检出率。通过F0轮廓的DP匹配来测量关键字与输入语音的韵律不相似度。检测关键字时，使用基于这两个指标的总分。根据F0模型进行平滑处理后，存储每个关键字的F0模板。在相同的检测率下，F0信息的引入使电视新闻语音的误报率降低了30% ~ 50%。提出了一种估计单词识别精度的方法，并与基于单词长度的估计方法进行了比较。通过对语言模型生成的音素序列进行语音识别仿真，计算出一种新的估计虚警次数的方法。基于仿真的方法在虚警估计方面表现出较好的性能。

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.