Topic segmentation of speech data based on keyword detection

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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.