Risk Based Lattice Cut Segmental Minimum Bayes-r
نویسنده
چکیده
Minimum Bayes-Risk (MBR) speech recognizers have been shown to give improvements over the conventional maximum a-posteriori probability (MAP) decoders through N-best list rescoring and A search over word lattices. Segmental MBR (SMBR) decoders simplify the implementation of MBR recognizers by segmenting the N-best lists or lattices over which the recognition is performed. We present a lattice cutting procedure that attempts to minimize the total Bayes-Risk of all word strings in the segmented lattice. We provide experimental results on the Switchboard conversational speech corpus showing that this segmentation procedure, in conjunction with SMBR decoding, gives modest but significant improvements over MAP decoders as well as MBR decoders on unsegmented lattices.
منابع مشابه
Risk based lattice cutting for segmental minimum Bayes-risk decoding
Minimum Bayes Risk (MBR) decoders improve upon MAP decoders by directly optimizing loss function of interest: Word Error Rate MBR decoding is expensive when the search spaces are large Segmental MBR (SMBR) decoding breaks the single utterance-level MBR decoder into a sequence of simpler search problems. – To do this, the N-best lists or lattices need to be segmented We present: A new lattice se...
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Minimum Bayes Risk (MBR) speech recognizers have been shown to yield improvements over the conventional maximum a-posteriori probability (MAP) decoders in the context of Nbest list rescoring and A search over recognition lattices. Segmental MBR (SMBR) procedures have been developed to simplify implementation of MBR recognizers, by segmenting the N-best list or lattice, to reduce the size of the...
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