@inproceedings{f03f7a8d1e6345f2b249b7c3dab4b92e,
title = "On using entropy information to improve posterior probability-based confidence measures",
abstract = "In this paper, we propose a novel approach that reduces the confidence error rate of traditional posterior probability-based confidence measures in large vocabulary continuous speech recognition systems. The method enhances the discriminability of confidence measures by applying entropy information to the posterior probability-based confidence measures of word hypotheses. The experiments conducted on the Chinese Mandarin broadcast news database MATBN show that entropy-based confidence measures outperform traditional posterior probability-based confidence measures. The relative reductions in the confidence error rate are 14.11% and 9.17% for experiments conducted on field reporter speech and interviewee speech, respectively.",
keywords = "Confidence measure, Continuous speech recognition, Entropy, Posterior probability",
author = "Chen, {Tzan Hwei} and Berlin Chen and Wang, {Hsin Min}",
year = "2006",
doi = "10.1007/11939993_48",
language = "English",
isbn = "3540496653",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "454--463",
booktitle = "Chinese Spoken Language Processing - 5th International Symposium, ISCSLP 2006, Proceedings",
note = "5th International Symposium on Chinese Spoken Language Processing, ISCSLP 2006 ; Conference date: 13-12-2006 Through 16-12-2006",
}