Title :
Use of Support Vector Machines through Linear-Polynomial (LP) Kernel for Speech Recognition
Author :
Sonkamble, B.A. ; Doye, Dharmpal D.
Author_Institution :
Dept. of Comput. Eng., Pune Inst. of Comput. Technol., Pune, India
Abstract :
The kernel functions are playing a very important role in machine learning. In this paper, the speech recognition problem is considered as a machine learning problem. The new kernel function called Linear-Polynomial kernel (LP) used to design the support vector machines for speech recognition for improving the generalization performance of speech recognition. The LP kernel performance is very good as compared to linear kernel and polynomial kernel and has improved the generalization performance ability of the speech recognition system. The One-versus-One approach is used for improving the systems efficiency.
Keywords :
learning (artificial intelligence); polynomials; speech recognition; support vector machines; LP kernel performance; linear-polynomial kernel; machine learning; one-versus-one approach; speech recognition; support vector machines; Kernel; Machine learning; Polynomials; Speech; Speech recognition; Support vector machines; LPC; Machine Learning; Optimal Hyperplane; Speech Recognition; Structural Risk Minimizatio; Support Vector Machines;
Conference_Titel :
Advances in Mobile Network, Communication and its Applications (MNCAPPS), 2012 International Conference on
Conference_Location :
Bangalore
Print_ISBN :
978-1-4673-1869-3
DOI :
10.1109/MNCApps.2012.14