Faster speaker enrollment for speaker verification systems based on MLPs by using discriminative cohort speakers method

Authors:
Tae-Seung Lee;Sung-Won Choi;Won-Hyuck Choi;Hyeong-Taek Park;Sang-Seok Lim;Byong-Won Hwang
Affiliations:
Hankuk Aviation University, School of Electronics, Telecommunication and Computer Engineering, Seoul, Korea;Hankuk Aviation University, School of Electronics, Telecommunication and Computer Engineering, Seoul, Korea;Hankuk Aviation University, School of Electronics, Telecommunication and Computer Engineering, Seoul, Korea;Hankuk Aviation University, School of Electronics, Telecommunication and Computer Engineering, Seoul, Korea;Hankuk Aviation University, School of Electronics, Telecommunication and Computer Engineering, Seoul, Korea;Hankuk Aviation University, School of Electronics, Telecommunication and Computer Engineering, Seoul, Korea
Venue:
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Year:
2003

Citing 4
Cited 0

Speech recognition: theory and C++ implementation

Speech recognition: theory and C++ implementation
A Method on Improvement of the Online Mode Error Backpropagation Algorithm for Pattern Recognition

PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Speaker background models for connected digit password speaker verification

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01

Quantified Score

Hi-index	0.00

Visualization

Abstract

Speaker verification system has been currently recognized as an efficient security facility due to its cheapness and convenient usability. This system has to achieve fast enrollment and verification in order to make a willing acceptance to users, as well as low error rate. For accomplishing such low error rate, multilayer perceptrons (MLPs) are expected to be a good recognition method among various pattern recognition methods for speaker verification. MLPs process speaker verifications in modest speed even with a low-capable hardware because they share their internal weights between all recognizing models. On the other hand, considerable speaker enrolling delay is made mainly due to the large population of background speakers for low verification error, since the increasing number of the background speakers prolongs the learning times of MLPs. To solve this problem, this paper proposes an approach to reduce the number of background speakers needed to learn MLPs by selecting only the back ground speakers nearby to an enrolling speaker. An experiment is conducted using an MLP-based speaker verification system and Korean speech database. The result of the experiment shows efficient improvement of 23.5% in speaker enrolling time.