  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.
