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Filtering web text to match target genres

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4 Author(s)
M. A. Marin ; University of Washington, Department of Electrical Engineering, Seattle, USA ; S. Feldman ; M. Ostendorf ; M. Gupta

In language modeling for speech recognition, both the amount of training data and the match to the target task impact the goodness of the model, with the trade-off usually favoring more data. For conversational speech, having some genre-matched text is particularly important, but also hard to obtain. This paper proposes a new approach for genre detection and compares different alternatives for filtering Web text for genre to improve language models for use in automatic transcription of broadcast conversations (talk shows).

Published in:

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

Date of Conference:

19-24 April 2009