Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 26, Issue 10, Pages 2574-2580Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2013.04.008
Keywords
Speech recognition; Support Vector Machine; Kernel function; Mercer kernel
Categories
Funding
- National Natural Science Foundation of China [61072087]
- Youth Foundation of Taiyuan University of Technology [2012L075]
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The kernel function is the core of the Support Vector Machine (SVM), and its selection directly affects the performance of SVM. There has been no theoretical basis on choosing a kernel function for speech recognition. In order to improve the learning ability and generalization ability of SVM for speech recognition, this paper presents the Optimal Relaxation Factor (ORF) kernel function, which is a set of new SVM kernel functions for speech recognition, and proves that the ORF function is a Mercer kernel function. The experiments show the ORF kernel function's effectiveness on mapping trend, bi-spiral, and speech recognition problems. The paper draws the conclusion that the ORF kernel function performs better than the Radial Basis Function (RBF), the Exponential Radial Basis Function (ERBF) and the Kernel with Moderate Decreasing (KMOD). Furthermore, the results of speech recognition with the ORF kernel function illustrate higher recognition accuracy. (C) 2013 Elsevier Ltd. All rights reserved.
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