Title
Type-2 fuzzy GMMs for robust text-independent speaker verification in noisy environments Type-2 fuzzy GMMs for robust text-independent speaker verification in noisy environments
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
conferenceObject
Publication
Los alamitos :Ieee computer soc ,
Subject
Computer. Automation
Source (journal)
Proceedings of the IAPR international conference on pattern recognition / IAPR International Conference on Pattern Recognition. - Los Alamitos
Source (book)
22nd International Conference on Pattern Recognition (ICPR), AUG 24-28, 2014, Swedish Soc Automated Image Anal, Stockholm, SWEDEN
Volume/pages
(2014) , p. 4531-4536
ISSN
1051-4651
ISBN
978-1-4799-5208-3
ISI
000359818004112
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
This paper proposes the use of the type-2 fuzzy GMM (T2FGMM) framework in order to improve the verification rates of the standard GMM-UBM text-independent speaker verification system in noisy environments. Based on type-2 fuzzy sets, the T2FGMM framework describes GMMs with uncertain parameters and provides likelihood intervals for them. The proposed method (T2F-GMM-UBM) estimates the parameter intervals using the noisy speeches from the speakers and the Bayesian estimation used in the standard GMM-UBM system. The proposed method was evaluated using the MIT Device Speaker Verification Corpus (MITDSVC) which contains speeches from 48 speakers recorded in three different locations: a quiet office, a mildly noisy lobby, and a busy street intersection. The Equal Error Rate (EER) was computed for each speaker and the mean and standard deviation were analyzed. Although the proposed method did not achieve better performance in the office location, significant improvements were achieved in both lobby and street intersection locations. The improvement in the lobby was 14.21% while in the street intersection location was 10.47%. The left tailed paired Rank Sign Wilcoxon Test was also performed in both locations and the p-values found were 0.0127 and 0.0230, respectively. The proposed method proved to have better performance in noisy environments compared to the standard GMM-UBM system.
E-info
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