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Title: A comparison of various adaptation methods for speaker verification with limited enrollment data
Authors: Mak, MW 
Hsiao, R
Mak, B
Keywords: Eigenvalues and eigenfunctions
Maximum likelihood estimation
Speaker recognition
Issue Date: 2006
Publisher: IEEE
Source: 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing : proceedings : May 14-19, 2006, Centre de Congrès Pierre Baudis, Toulouse, France, p. I How to cite?
Abstract: One key factor that hinders the widespread deployment of speaker verification technologies is the requirement of long enrollment utterances to guarantee low error rate during verification. To gain user acceptance of speaker verification technologies, adaptation algorithms that can enroll speakers with short utterances are highly essential. To this end, this paper applies kernel eigenspace-based MLLR (KEMLLR) for speaker enrollment and compares its performance against three state-of-the-art model adaptation techniques: maximum a posteriori (MAP), maximum-likelihood linear regression (MLLR), and reference speaker weighting (RSW). The techniques were compared under the NIST2001 SRE framework, with enrollment data vary from 2 to 32 seconds. Experimental results show that KEMLLR is most effective for short enrollment utterances (between 2 to 4 seconds) and that MAP performs better when long utterances (32 seconds) are available
ISBN: 1-4244-0469-X
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2006.1660174
Appears in Collections:Conference Paper

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