This paper reports on topic extraction in Japanese broadcastnews speech. We studied, using continuous speech recognition, the extraction of several topic-words h m broadcast-news. A combination of multiple topic-words represents the content of the news. This is a more detailed and more flexible approach than using a single word or a single category. A topic-extraction model shows the degree of relevance between each topic-word and each word in the article. For all words in an article, topic-words which have high total relevance score are extracted. We trained the topicextraction model with five years of newspapers, using the frequency of topic-words taken f " headlines and words in articles. The degree of relevance between topic-words and words in articles is calculated on the basis of statistical measures, i.e., mutual information or the X2-value. In topicextraction experiments for recognized broadcast-news speech, we extracted five topic-words h m the IO-best hypotheses using a X2-based model and found that 76.6% of them agreed with the topic-words chosen by subjects.