In this paper, we report on language modeling and acoustic modeling studies for Japanese broadcast news speech recognition. We constructed a language model that reduces recognition errors by utilizing context-dependent readings of Japanese characters. We also introduced filled-pause modeling into the language model. To improve the model’s performance for a series of sentences spoken by one speaker, an on-line incremental speaker adaptation was combined with automatic detection of speaker changes. By incorporating all the above methods, we achieved a 25.1% reduction in word error rate over the baseline results. This paper also reports on our preliminary studies on topic extraction and summarization of broadcast-news speech.