Topic extraction based on continuous speech recognition in broadcast-news speech

Abstract

The 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 of relevance between each topic word and each word in the articles. For all words in an 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 of statistical measures, i.e., mutual information or the /spl chi//sup 2/ value. In topic extraction experiments for recognized broadcast news speech, we extracted five topic words using a /spl chi//sup 2/ based model and found that 75% of them agreed with topic words chosen by subjects.