Abstrait

Feature Selection Algorithm for Automatic Speech Recognition Based On Fuzzy Logic

Catherine J Nereveettil, M.Kalamani, Dr.S.Valarmathy

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software. But despite of all these advances, machines cannot match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. This paper present the viability of Mel Frequency Cepstral coefficient Algorithm to extract features and Fuzzy Inference System model for feature selection, by reducing the dimensionality of the extracted features.There is an increasing need for a new Feature selection method, to increase the processing rate and recognition accuracy of the classifier, by selecting the discriminative features.Hence a Fuzzy Inference system model is used selecting the optimal features from speech vectors which are extracted using MFCC. The work has been done on MATLAB 13a and experimental results show that system is able to reduce word error rate at sufficiently high accuracy