Large vocabulary continuous speech recognition using word graphs
Xavier L. Aubert, Hermann Ney · 2002
We address the problem of using word graphs (or lattices) for the integration of complex knowledge sources like long span language models or acoustic cross-word models, in large vocabulary continuous speech recognition. A method for efficiently constructing a word graph is reviewed and two ways of exploiting it are presented. By assuming the word pair approximation, a phrase level search is possible while in the other case a general graph decoder is set up. We show that the predecessor-word identity provided by a first bigram decoding might be used to constrain the word graph without impairing the next pass. This procedure has been applied to 64 k-word trigram decoding in conjunction with an incremental unsupervised speaker adaptation scheme. Experimental results are given for the North American Business corpus used in the November '94 evaluation.