Title
Unsupervised Topic Adaptation for Lecture Speech Retrieval
Abstract
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.
Year
Venue
Keywords
2004
INTERSPEECH
automatic speech recognition,language model,information retrieval system,speech recognition
DocType
Volume
ISSN
Journal
cs.CL/0407027
Proceedings of the 8th International Conference on Spoken Language Processing (ICSLP 2004), pp.2957-2960, Oct. 2004
Citations 
PageRank 
References 
1
0.36
5
Authors
4
Name
Order
Citations
PageRank
Atsushi Fujii148659.25
Katunobu Itou231944.36
Tomoyosi Akiba317629.08
Tetsuya Ishikawa422630.46