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CORR
2004
Springer

Unsupervised Topic Adaptation for Lecture Speech Retrieval

11 years 5 months ago
Unsupervised Topic Adaptation for Lecture Speech Retrieval
We are developing a cross-media information retrieval system, in which users can view specific segments of lecture videos by submitting text queries. To produce a text index, the audio track is extracted from a lecture video and a transcription is generated by automatic speech recognition. In this paper, to improve the quality of our retrieval system, we extensively investigate the effects of adapting acoustic and language models on speech recognition. We perform an MLLR-based method to adapt an acoustic model. To obtain a corpus for language model adaptation, we use the textbook for a target lecture to search a Web collection for the pages associated with the lecture topic. We show the effectiveness of our method by means of experiments.
Atsushi Fujii, Katunobu Itou, Tomoyosi Akiba, Tets
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2004
Where CORR
Authors Atsushi Fujii, Katunobu Itou, Tomoyosi Akiba, Tetsuya Ishikawa
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