Improvements in Japanese Broadcast News Transcription

This paper reports on recent improvements in Japanese broadcast news transcription and topic extraction. We constructed a language model that depends on the readings of words in order to prevent recognition errors caused by context-dependent readings of Japanese characters. We also introduced interjection 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 applied. We investigated a method for extracting topic-words from the speech recognition results that was based on a significance measure. This paper also proposes a new formulation for speech recognition/understanding systems, in which the a posteriori probability of a message that the speaker intends to address given an observed acoustic sequence is maximized. We applied the formulation to rescoring the recognition hypotheses.