Filtering web text to match target genres
M. A. Marin, Sergey Feldman, Mari Ostendorf, M. Gupta · 2009
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).