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- W2019921918 abstract "Recently, a variety of confidence measures for utterance verification has been studied to improve speech recognition performance by rejecting out-of-vocabulary inputs. Most of the conventional confidence measures for utterance verification are based primarily on hypothesis testing or an approximated posterior probability, and their performances depend on the robustness of an alternative hypothesis or the prior probability. We introduce a novel confidence measure called a search confusion rate (SCR), which does not require an alternative hypothesis or the approximation of posterior probability. Our confusion-based approach shows better performance in additive noise-corrupted speech as well as in clean speech." @default.
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- W2019921918 date "2005-08-09" @default.
- W2019921918 modified "2023-09-23" @default.
- W2019921918 title "Utterance Verification Using Search Confusion Rate and Its N-Best Approach" @default.
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- W2019921918 doi "https://doi.org/10.4218/etrij.05.0205.0027" @default.
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