LANGUAGE MODEL ADAPTATION FOR ACADEMIC LECTURES USING CHARACTER RECOGNITION RESULT OF PRESENTATION SLIDES

For automatic speech recognition (ASR) of lectures, texts of presentation slides are expected to be useful for adapting a language model, while slide texts are not always available in a machine-readable form. In this paper, we propose a language model adaptation framework that uses character recognition results of slide images in a lecture video. Since character recognition results contain many errors, we introduce a filtering method based on morphological and topic information. Then we perform linear interpolation of the baseline language model with the filtered results and also relevant texts which are selected automatically from a text database using the filtered results. We further conduct a cache-based adaptation method on the resulting language model, in which keywords in the filtered results are cached and used to boost the word probability. In an experimental evaluation over real lectures, we obtained a significant improvement of ASR performance by this adaptation framework.