Task-Specific Minimum Bayes-Risk Decoding using Learned Edit Distance

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“Task-Specific Minimum Bayes-Risk Decoding using Learned Edit Distance” by I. Shafran and W. Byrne. In Proc. of the International Conference on Spoken Language Processing, 2004.

Abstract

This paper extends the minimum Bayes-risk framework to incorporate a loss function specific to the task and the ASR system. The errors are modeled as a noisy channel and the parameters are learned from the data. The resulting loss function is used in the risk criterion for decoding. Experiments on a large vocabulary conversational speech recognition system demonstrate significant gains of about 1% absolute over MAP hypothesis and about 0.6% absolute over untrained lossfunction. The approach is general enough to be applicable to other sequence recognition problems such as in Optical Character Recognition (OCR) and in analysis of biological sequences.

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BibTeX entry:

@inproceedings{icslp04_tsmbr,
   author = {I. Shafran and W. Byrne},
   title = {Task-Specific Minimum {B}ayes-Risk Decoding using Learned Edit
	Distance},
   booktitle = {Proc. of the International Conference on Spoken Language
	Processing},
   pages = {(4 pages)},
   year = {2004}
}

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