Paper Info
Title
Exploiting Non-Target Region Information for Confidence Measure Based on Bayesian Information Criterion
Abstract
In this paper appropriate confidence measures (CMs) are investigated for Mandarin command word recognition, both in the so-called target region and non-target region, respectively. Here the target region refers to the recognized speech part of command word while the non-target region refers to the recognized silence part. It shows that exploiting extra information in the non-target region can effectively complement the traditional CM which usually focus on the target region. Furthermore, when analyzing the non-target region in a more theoretical way, where Bayesian information criterion (BIC) is employed to locate more precise boundary in the non-target region, even more improvement is achieved. In two different Mandarin telephone command word tasks, more than 20% relative reduction of equal error rate (EER) is obtained.
Year
DOI
Venue
2008
10.1109/CHINSL.2008.ECP.69
ISCSLP
Keywords
Field
DocType
bayesian information criterion,speech recognition,maximum likelihood estimation,equal error rate,index terms— speech recognition,non-target region information,word recognition,confidence measure,hidden markov models,indexing terms,speech,testing,bayesian methods,databases
Confidence measures,Bayesian information criterion,Pattern recognition,Computer science,Word recognition,Word error rate,Maximum likelihood,Speech recognition,Artificial intelligence,Hidden Markov model,Mandarin Chinese,Bayesian probability
Conference
ISBN
Citations 
PageRank 
978-1-4244-2943-1
0
0.34
References 
Authors
5
6
Name
Order
Citations
PageRank
Cong Liu1747.21
Yu Hu217317.03
Xiong-Guo Lei300.34
Zhi-Guo Wang400.34
Li-Rong Dai51070117.92
Ren-Hua Wang634441.36