Topic recognition for news speech based on keyword spotting
نویسندگان
چکیده
This paper describes topic identi cation for Japanese TV news speech based on the keyword spotting technique. Three thousands of nouns are selected as keywords which contribute to topic identi cation, based on criterion of mutual information and a length of the word. This set of the keywords identi ed the correct topic for 76.3% of articles from newspaper text data. Further, we performed keyword spotting for TV news speech and identi ed the topics of the spoken message by calculating possibilities of the topics in terms of an acoustic score of the spotted word and a topic probability of the word. In order to neutralize e ect of false alarms, bias of the topics in the keyword set is removed. Topic identi cation rate is 66.5% assuming that identi cation is correct if the correct topic is included in the top three topics. The removal of the bias improved the identi cation rate by 6.1%.
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