Bayes-based confidence measure in speech recognition
| Venue: | IEEE Signal Processing Letters |
| Citations: | 2 - 2 self |
BibTeX
@ARTICLE{Yoma_bayes-basedconfidence,
author = {Néstor Becerra Yoma and Jorge Carrasco and Carlos Molina},
title = {Bayes-based confidence measure in speech recognition},
journal = {IEEE Signal Processing Letters},
year = {},
pages = {745--748}
}
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Abstract
In this paper Bayes-based confidence measure (BBCM) in speech recognition is proposed. BBCM is applicable to any standard word feature and makes use of information about the speech recognition engine performance. In contrast to ordinary confidence measures, BBCM is a probability, which is interesting itself from the practical and theoretical point of view. If applied with word density confidence measure (WDCM), BBCM dramatically improves the discrimination ability of the false acceptance curve when compared to WDCM itself.
Citations
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| 1 | Language model accuracy and uncertainty in noise canceling in the stochastic weighted Viterbi algorithm - Yoma, Brito, et al. |
