Speaker Recognition Based on KPCA and KFCM

DOI

10.2991/meic-15.2015.44How to use a DOI?

Keywords

SpeakerRecognition; KPCA; KFCM; VQ; MCS

Abstract

Speaker recognition system can identify a certain person using speech analysis. Recent advances in speech processing techniques improve the recognition rate. In this paper, an efficient speaker recognition system is proposed. Firstly, a KPCA-based feature selection approach is adopted to get the efficiently reduced dimension of feature vectors and improve clustering performance. Secondly, it has been known that the KFCM has a good superiority in clustering Non-linear and asymmetric samples and it can alleviate the negative influence of the noise and outliers. Thus KFCM clustering algorithm is applied on the selected feature samples to give out a series of clustering centers in feature space, which doubtless can represent the training set in a sense. An analysis is also provided by performing different experiments on the methods that influence the recognition rate. The experiment result shows that the proposed method can resolve the reduce the recognition error rate effectively.

Copyright

© 2015, 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/).

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TY  - CONF
AU  - Jian Wang
AU  - Yuanyuan Zhang
PY  - 2015/04
DA  - 2015/04
TI  - Speaker Recognition Based on KPCA and KFCM
BT  - Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 181
EP  - 184
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-15.2015.44
DO  - 10.2991/meic-15.2015.44
ID  - Wang2015/04
ER  -