SVM and ANN: A comparative evaluation

Support vector machines (SVMs) are among the most robust classifiers for the purpose of speech recognition. This paper compares one of the more contemporary methods of classification, artificial neural network (ANN) with support vector machines and draws conclusions based on a comparison of accuracy. The neural network is a pattern network for variable hidden neurons and transfer functions. C- Support vector classifier is used with three different kernels and kernel parameters. MFCC has been used as the feature extraction technique for a noiseless database of 50 independent speakers. The results were found to be best for SVM with RBF kernel in comparison to bi-quadratic polynomial and sigmoid kernels and pattern network.