Automatic speech recognition using optimal selection of features based on hybrid ABC-PSO

Abstract

Optimal Speech feature extraction which tries to acquire a parametric illustration of an input speech signal plays a vital role in the overall performance of an Automatic Speech Recognition (ASR) system. A good feature extraction technique along with feature selection algorithm should capture the important features of the signal and also should reject some irrelevant features. Feature selection is a critical task which can influence the performance of pattern classification and recognition system. In this paper, we have presented a hybrid evolutionary algorithm called Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) for optimal feature extraction and selection strategy. The proposed method consists of three stages, preprocessing, feature extraction and selection, and recognition. Initially, the preprocessing is done by wiener filter to reduce the noise level. Next, we extract eight type of statistical and acoustic features in the feature stage. The optimal set of extracted features is selected by using hybrid ABC-PSO algorithm. Finally, these optimized features are used for training the Support Vector Machine (SVM) classifier and based on these optimized features of the given input speech signal the corresponding text is displayed as the output. The proposed ASR is implemented on the MATLAB working platform and the experimental results show that overall performance of the system is high and the proposed hybrid algorithm is best suited for speech recognition.