Large vocabulary continuous speech recognition of Wall Street Journal data
Xavier L. Aubert, Christian Dugast, Hermann Ney, Volker Steinbiss · 2002
We report on recent developments of the Philips large vocabulary speech recognition system and on our experiments with the Wall Street Journal (WSJ) corpus. A two-pass decoding has been devised that allows an easy integration of more complex language models. First, a word lattice is produced using a time synchronous beam search with a bigram language model. Next, a higher-order language model is applied to the lattice at the phrase level. The conditions insuring the validity of this approach are explained and practical results for trigram demonstrate its usefulness. The main system development stages on WSJ data are presented and our final recognizers are evaluated on Nov. '92 and Nov. '93 test-data for both 5 K and 20 K vocabularies.>