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Automatic Speech Transcription and Archiving System using the Corpus of Spontaneous Japanese

The target of automatic speech recognition (ASR) research has been shifted from read speech to spontaneous speech. The technology will realize automatic transcription (and translation) of lectures and meetings. In Japan, ”Spontaneous Speech” project has been conducted in last five years, and we set up the huge ”Corpus of Spontaneous Japanese (CSJ)”, which consists of over 2000 speeches (500 hours) and 7M words. The paper firstly addresses the current state of speech recognition using this corpus. It is shown that the large-scale corpus had strong impact in training acoustic and language models considering morphological and pronunciation variations which are characteristic to spontaneous Japanese. Unsupervised adaptation of these models and the speaking rate is also effective, and we obtained word accuracy of 78.0%. Then, an intelligent archiving system of lectures based on automatic transcription and indexing is introduced. Transcriptions are automatically edited for improving readability, and key sentences are indexed based on statistically-derived discourse markers and topic words. Thus, we realize efficient browsing of lecture audio archives.