Improved Support Vector Pre-extracting Algorithm in Speech Recognition Application

Authors

Hao Rui, 1. College of Information Management, Shanxi University of Finance & Economics, Taiyuan 030006, China; ;
Yanbo Niu, 2. College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China; ;
Xiu Lei, 3. College of Statistics, Shanxi University of Finance & Economics, Taiyuan 030006, China;

Abstract

Abstract: Support vector machine (SVM) training is difficult for large-scale data set of speech recognition. A new SVM pre-extracting algorithm was proposed. On the one hand, kernel Fuzzy C-Means clustering was separately performed on each class of original data set. All the cluster centers were as a representative set of each class. On the other hand, according to the geometric distribution of support vectors and combined with the classification strategy of one-versus-one for SVM multi-class classification algorithm, boundary samples were extracted as support vectors for SVM to training and prediction. The algorithm was applied to embedded speech recognition system. Experiments indicate that this method improves the efficiency of training but also maintains the high recognition rate.

Recommended Citation

Rui, Hao; Niu, Yanbo; and Lei, Xiu (2020) "Improved Support Vector Pre-extracting Algorithm in Speech Recognition Application," Journal of System Simulation: Vol. 27: Iss. 11, Article 13.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss11/13

First Page

2714

Revised Date

2015-03-30

DOI Link

https://doi.org/

Last Page

2721

CLC

TP391.9

Recommended Citation

Hao Rui, Niu Yanbo, Xiu Lei. Improved Support Vector Pre-extracting Algorithm in Speech Recognition Application[J]. Journal of System Simulation, 2015, 27(11): 2714-2721.