NDLI: Maximum a posteriori linear regression for speaker recognition

Content Provider	IEEE Xplore Digital Library
Author	Zhang, Xiang Wang, Haipeng Xiao, Xiang Zhang, Jianping Yan, Yonghong
Copyright Year	2010
Description	Author affiliation: ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China (Zhang, Xiang; Wang, Haipeng; Xiao, Xiang; Zhang, Jianping; Yan, Yonghong)
Abstract	Recently, using maximum likelihood linear regression (MLLR) transforms as the features for SVM based speaker recognition has been proposed. This can achieve performance comparable to that obtained with state-of-the-art approaches. In this paper, we focus on calculating the transforms based on a GMM universal background model (UBM). Rather than estimating the transforms using maximum likelihood criterion, we describe a new feature extraction technique for speaker recognition based on maximum a posteriori linear regression (MAPLR). This work is enriched by a proposed multi-class technique, which clusters the Gaussian mixtures into regression classes and estimates a different transform for each class. All the transforms of all the classes for a given utterance are concatenated into a supervector for SVM classification. Experiments on a NIST 2008 SRE corpus show that the speaker recognition system using MAPLR outperforms MLLR, and the multi-class approach can also bring significant gains for MAPLR system.
Starting Page	4542
Ending Page	4545
File Size	165481
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495579
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Speaker recognition Transforms Adaptation model Speech Training Mathematical model SVM MLLR MAPLR
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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