Abstract
We explore a speech-based topic classification approach. We generate the transcript of input video lecture based on speech recognition technology and identify the topic by comparing its term-based vector with topic models. The preliminary experiment result shows that the speech-based topic classification works well, with its performance comparable to one that directly uses manual transcripts. The approach also shows robustness against speech recognition errors up to 40.6%.
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© 2012 Springer-Verlag Berlin Heidelberg
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Park, J., Kim, J. (2012). Classifying Topics of Video Lecture Contents Using Speech Recognition Technology. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2012. Lecture Notes in Computer Science, vol 7315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30950-2_125
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DOI: https://doi.org/10.1007/978-3-642-30950-2_125
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30949-6
Online ISBN: 978-3-642-30950-2
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