Research of the speaker verification based on the SVM-GMM mixture model
Cui Xuan1, Deng Bo, Zhuang Wen
1School of Mathematics & Computer Engineering , Xihua University
Available Online October 2007.
- 10.2991/iske.2007.215How to use a DOI?
- Speaker verification, Gaussian mixed model, Support vector machines, SVM-GMM mixture model
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.
- © 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/).
Cite this article
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 -