Research of the speaker verification based on the SVM-GMM mixture model

DOI

10.2991/iske.2007.215How to use a DOI?

Keywords

Speaker verification, Gaussian mixed model, Support vector machines, SVM-GMM mixture model

Abstract

We put forward a new SVM-GMM mixture model to improve recognition rate of the speaker verification system in the paper. Support vector machines (SVM) and Gaussian mixture model (GMM) are widely applied to the speaker verification, but both have some disadvantages. We present a new approach for speaker verification based on their feature. The new model introduce the output of the Gaussian mixture model to Support vector machines, in order to adjust the probabilistic output of the support vector of machines. It can compliment support vector machines with probabilistic information. The experiments have proved that SVM-GMM mixture model can effective enhance the recognition rate of the speaker verification system.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Cui Xuan
AU  - Deng Bo
AU  - Zhuang Wen
PY  - 2007/10
DA  - 2007/10
TI  - Research of the speaker verification based on the SVM-GMM mixture model
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1264
EP  - 1268
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.215
DO  - 10.2991/iske.2007.215
ID  - Xuan2007/10
ER  -