Lattice Segmentation and Minimum Bayes Risk Discriminative Training for Large Vocabulary Continuous Speech Recognition

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Lattice Segmentation and Minimum Bayes Risk Discriminative Training for Large Vocabulary Continuous Speech Recognition” by V. Doumpiotis and W. Byrne. Speech Communication, no. 2, 2005, pp. 142-160 (19 pages).

Abstract

Lattice segmentation techniques developed for Minimum Bayes Risk decoding in large vocabulary speech recognition tasks are used to compute the statistics for discriminative training algorithms that estimate HMM parameters so as to reduce the overall risk over the training data. New estimation procedures are developed and evaluated for small vocabulary and large vocabulary recognition tasks, and additive performance improvements are shown relative to maximum mutual information estimation. These relative gains are explained through a detailed analysis of individual word recognition errors.

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BibTeX entry:

@article{lsegmbrdt,
   author = {V. Doumpiotis and W. Byrne},
   title = {Lattice Segmentation and Minimum {B}ayes Risk Discriminative
	Training for Large Vocabulary Continuous Speech Recognition},
   journal = {Speech Communication},
   number = {2},
   pages = {142--160 (19 pages)},
   year = {2005},
   url = {http://dx.doi.org/10.1016/j.specom.2005.07.002}
}

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