NDLI: Vocal Tract Length Normalization factor based speaker-cluster UBM for speaker verification

Content Provider	IEEE Xplore Digital Library
Author	Sarkar, A.K. Rath, S.P. Umesh, S.
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, Indian Institute of Technology Madras, India 600 036 (Sarkar, A.K.; Rath, S.P.; Umesh, S.)
Abstract	In speaker verification task requires some sort of background model for the system to make decision. Most of the cases, a speaker independent large Gaussian Universal Background Model (GMM-UBM) is used. In this paper, we propose to use a Speaker Cluster-wise UBM (SC-UBM) for a group of target speakers. In this method, the target speakers are clustered into group based on their similarity in Vocal Tract Length Normalization (VTLN) parameter. The VTLN parameter depends on the physiological structure of human speech production system. Hence, the group of speakers with same VTLN factor represent a speaker with unique characteristic. The SC-UBMs are derived from GMM-UBM with Maximum Likelihood Linear Regression (MLLR) by pooling data from the specific group of target speakers. The speaker dependent models are then adapted from their respective SC-UBM using Maximum a Posteriori (MAP) method. During verification, the log likelihood ratio for the claimant is calculated with respect to the corresponding group specific UBM. The comparative study are performed on NIST 2004 SRE in core condition. The SC-UBM system reduced equal error rate (EER) by 9% over the GMM-UBM system.
Starting Page	1
Ending Page	5
File Size	670853
Page Count	5
File Format	PDF
ISBN	9781424463831
e-ISBN	9781424463862
DOI	10.1109/NCC.2010.5430207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-01-29
Publisher Place	INDIA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Loudspeakers Production systems Error analysis Maximum likelihood linear regression Humans Training data NIST Spatial databases Speaker recognition Automatic speech recognition
Content Type	Text
Resource Type	Article

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