Morlet-Kernel Principal Component Analysis Features for Speech Recognition

Abstract

The aim of this paper is to present the application of Morlet wavelet to extract the speech features in place of MFCC features. KPCA is applied for selecting and reducing the large features obtained from Morlet wavelet. NLMS (Normalized Least Mean Square) filter is used to reduce additive noise levels ranging from ±5 dB to ±15 dB. Features are modeled using Ensembled Support Vector Machine classification model for FSDD and Kannada multi speaker data sets. The comparative results are discussed over logistic regression model. The proposed model reduces the noise with 99% of recognition rate for isolated words. The efficiency of ensembled classification model is explored.