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Connectionist language modeling for large vocabulary continuous speech recognition
2002
IEEE International Conference on Acoustics Speech and Signal Processing
This paper describes ongoing work on a new approach for language modeling for large vocabulary continuous speech recognition. Almost all state-of-the-art systems use statistical ¢ -gram language models estimated on text corpora. One principle problem with such language models is the fact that many of the ¢ -grams are never observed even in very large training corpora, and therefore it is common to back-off to a lower-order model. In this paper we propose to address this problem by carrying out
doi:10.1109/icassp.2002.5743830
dblp:conf/icassp/SchwenkG02
fatcat:pjmzpemhizeazoi5atfubxtjqy