The area of speech recognition is one of the interesting field in speech signal processing. Achieving accuracy and robustness is a very difficult constraint to various environmental factors. Progressive work and reviews in the speech recognition application has been adopted using fuzzy, as one of the technique to improve the recognition accuracies. This review paper reviews the various concepts of fuzzy technique and its applications to speech signal processing area. Since the nature of speech signal is vague, it does not pocess uniformity at all time intervals. To deal with this vagueness and uncertainties, many researchers have suggested fuzzy is one of the better technique to analyze the speech signals. This paper presents the literature work available related to speech recognition using fuzzy techniques.

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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.

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