Technical Program

Paper Detail

Paper:	SLP-P14.10
Session:	Speaker Recognition: Models and Methods
Time:	Thursday, May 18, 14:00 - 16:00
Presentation:	Poster
Topic:	Speech and Spoken Language Processing: Speaker Verification
Title:	A Comparison of Various Adaptation Methods for Speaker Verification with Limited Enrollment Data
Authors:	Man-Wai Mak, Hong Kong Polytechnic University, Hong Kong SAR of China; Roger Hsiao, Carnegie Mellon University, United States; Brian Mak, Hong Kong University of Science and Technology, Hong Kong SAR of China
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.