Abstract
A Neuro Fuzzy Classifier with Linguistic Hedges for Speech Recognition
Highlights
Speech classification and recognition applications faces a lot of challenges due to the presence of various non-linearity’s and noises present in the speech signal and environments
There is a average increase in classification accuracy from .22% to 5% for Free Spoken Digit Dataset (FSDD) data set as shown in table 3.In this work Linguistic Hedge (LH)-NFC is tried on the Kannada data set for the first time
Optimal features are obtained by applying fuzzy entropy technique
Summary
Speech classification and recognition applications faces a lot of challenges due to the presence of various non-linearity’s and noises present in the speech signal and environments. As the variability’s of speech vary from one person to another, we require efficient computation models than ordinary conventional models. Adaptive Neuro Fuzzy Systems [1], are used to construct an efficient fuzzy predictor model for speech recognition for predicting the speech classes effectively. Due to the fuzziness in the overlapped classes, conventional classifiers fail to classify the speech data. To overcome these problems Neuro Fuzzy Classifiers are proposed. To increase the classification performance of the overlapped classes, the concept of fuzzy
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