Summary
|
For Full-Text PDF, please login, if you are a member of IEICE, or go to Pay Per View on menu list, if you are a nonmember of IEICE. |
|
Enhancing the Robustness of the Posterior-Based Confidence Measures Using Entropy Information for Speech Recognition Yanqing SUN Yu ZHOU Qingwei ZHAO Pengyuan ZHANG Fuping PAN Yonghong YAN Publication IEICE TRANSACTIONS on Information and Systems Vol.E93-D No.9 pp.2431-2439 Publication Date: 2010/09/01 Online ISSN: 1745-1361 DOI: 10.1587/transinf.E93.D.2431 Print ISSN: 0916-8532 Type of Manuscript: Special Section PAPER (Special Section on Processing Natural Speech Variability for Improved Verbal Human-Computer Interaction) Category: Robust Speech Recognition Keyword: OOV, speech recognition, confidence measure, entropy information, phoneme-level posterior, Full Text: PDF(1.2MB)>>
Summary: In this paper, the robustness of the posterior-based confidence measures is improved by utilizing entropy information, which is calculated for speech-unit-level posteriors using only the best recognition result, without requiring a larger computational load than conventional methods. Using different normalization methods, two posterior-based entropy confidence measures are proposed. Practical details are discussed for two typical levels of hidden Markov model (HMM)-based posterior confidence measures, and both levels are compared in terms of their performances. Experiments show that the entropy information results in significant improvements in the posterior-based confidence measures. The absolute improvements of the out-of-vocabulary (OOV) rejection rate are more than 20% for both the phoneme-level confidence measures and the state-level confidence measures for our embedded test sets, without a significant decline of the in-vocabulary accuracy. | ||||||||||
| ||||||||||
