α-Gaussian mixture modelling for speaker recognition

Authors:
Dalei Wu;Ji Li;Haiqing Wu
Affiliations:
Department of Computer Science and Engineering, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3;Department of Computer Science and Engineering, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3;Department of Computer Science and Engineering, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3
Venue:
Pattern Recognition Letters
Year:
2009

Citing 5
Cited 3

Speaker identification and verification using Gaussian mixture speaker models

Speech Communication
Corpora for the evaluation of speaker recognition systems

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Integration of Stochastic Models by Minimizing α-Divergence

Neural Computation
Parameter estimation for α-gmm based on maximum likelihood criterion

Neural Computation
MLP internal representation as discriminative features for improved speaker recognition

NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing

Unfolding speaker clustering potential: a biomimetic approach

MM '09 Proceedings of the 17th ACM international conference on Multimedia
Comparison of the impact of some Minkowski metrics on VQ/GMM based speaker recognition

Computers and Electrical Engineering
Comparison of clustering methods: A case study of text-independent speaker modeling

Pattern Recognition Letters

Quantified Score

Hi-index	0.10

Visualization

Abstract

Gaussian mixture model is the conventional approach employed in speaker recognition tasks. Although it is efficient to model specific speaking characteristics of a speaker, especially in quiet environments, its performance in noisy conditions is still far from the human cognitive process. Recently, a new method of @a-integration of stochastic models has been proposed based on psychophysical experiments that suggests @a-integration is used in a human brain. In this paper, we proposed a method to extend the conventional GMM to the @a-integrated GMM (@a-GMM) to model personal speaking traits. Model parameters were re-estimated recursively based on a given data set. The experiments showed that the new approach significantly outperforms the traditional method, especially on telephony speech.