A Neuro Fuzzy Classifier with Linguistic Hedges for Speech Recognition

Abstract

Fuzzy classification is the task of partitioning a feature space into fuzzy classes. A Neuro fuzzy classifier with linguistic hedges is proposed for noisy and clean speech classification. The linguistic Hedges are used to improve the meaning of fuzzy rules up to secondary level. Fuzzy entropy is applied to select optimal features of MFCC for framing the rules for designing the fuzzy inference system. Results obtained from the proposed classifier is compared over conventional and Neuro Fuzzy Classifier. The classification rates of the proposed model is better than other traditional and conventional fuzzy classifiers. 0.22 to 5% improved classification accuracy is observed for the FSDD dataset. And 5% to 11% of improved classification accuracy is observed for Kannada dataset. From this study it is identified that LH plays a major role in classifying the overlapped classes of data.