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This paper reports on topic extraction in Japanese broadcast-news speech. We studied, using continuous speech recognition, the extraction of several topic-words from broadcast-news. A combination of multiple topic-words represents the content of the news. This is more detailed and more flexible than a single word or a single category. A topic-extraction model shows the degree o f relevance between each topic-word and each word in the articles. For all walrds in a n article, topic-words which have high total relevance score are extracted from the article. We trained the topic-extraction model with five years of newspapers, using the frequency of topic-words taken from headlines and words in articles. The degree of relevance between topic-words and words in articles is calculated on the basis o f statistical measures, i.e., mutual information or the X*-value. In topic-extraction experiments for recognized broadcast-news speech, we extracted five topic-words using a X2-based model and found that 75% of them agreed with topic-words chosen by subjects.

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