Unsupervised Topic Adaptation for Lecture Speech Retrieval

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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.
4 pages, Proceedings of the 8th International Conference on Spoken Language Processing (to appear)

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Computation and Language, I.2.7; H.3.3; H.5.1

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http://salesiana.dossiersoluciones.com/handle/123456789/32241

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0407027
 http://arxiv.org/abs/cs/0407027
 Proceedings of the 8th International Conference on Spoken Language Processing (ICSLP 2004), pp.2957-2960, Oct. 2004

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arXiv