Boosting Minimum Bayes Risk Discriminative Training
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چکیده
A new variant of AdaBoost is applied to a Minimum Bayes Risk discriminative training procedure that directly aims at reducing Word Error Rate for Automatic Speech Recognition. Both techniques try to improve the discriminative power of a classifier and we show that can be combined together to yield even better performance on a small vocabulary continuous speech recognition task. Our results also demonstrate an interesting learning behavior that has never been studied previously in speech recognition.
منابع مشابه
Lattice segmentation and minimum Bayes risk discriminative training
Modeling approaches are presented that incorporate discriminative training procedures in segmental Minimum Bayes-Risk decoding (SMBR). SMBR is used to segment lattices produced by a general automatic speech recognition (ASR) system into sequences of separate decision problems involving small sets of confusable words. We discuss two approaches to incorporating these segmented lattices in discrim...
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A modeling approach is presented that incorporates discriminative training procedures within segmental Minimum Bayes-Risk decoding (SMBR). SMBR is used to segment lattices produced by a general automatic speech recognition (ASR) system into sequences of separate decision problems involving small sets of confusable words. Acoustic models specialized to discriminate between the competing words in...
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