Improvements of search error risk minimization in viterbi beam search for speech recognition
Abstract This paper describes improvements in a search error risk min-imization approach to fast beam search for speech recognition.In our previous work, we proposed this approach to reducesearch errors by optimizing the pruning criterion. While con-ventional methods use heuristic criteria to prune hypotheses,our proposed method employs a pruning function that makesa more precise decision using rich features extracted from eachhypothesis. The parameters of the function can be estimatedto minimize a loss function based on the search error risk. Inthis paper, we improve this method by introducing a modiﬁedloss function, arc-averaged risk, which potentially has a highercorrelation with actual error rate than the original one. We alsoinvestigate various combinations of features. Experimental re-sults show that further search error reduction over the originalmethod is obtained in a 100K-word vocabulary lecture speechtranscription task.IndexTerms: speech recognition, beam search, pruning, searcherror, WFST