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.
Original language | English |
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Title of host publication | 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings |
Volume | 1 |
Publication status | Published - 1 Dec 2006 |
Event | 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France Duration: 14 May 2006 → 19 May 2006 |
Conference
Conference | 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 |
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Country/Territory | France |
City | Toulouse |
Period | 14/05/06 → 19/05/06 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering