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Using Neuro-Fuzzy System to Improve Speech Recognition

This paper presents an efficient speech recognition system for isolated Arabic words based ‎on the combination of a feature extraction by wavelet transform, subtractive clustering and adaptive neuro-fuzzy inference system (ANFIS) to improve the recognition rate. The data set was created from extracting the coefficients wavelet ‎transform parameters‎for some of speech signal (Arabic digits and other Arabic words) which was collected from four ‎individuals in various time intervals. The feature extraction is used as input of the subtractive clustering to put the data in a group of clusters. Also it is used as an input of the neural network in ANFIS. The initial fuzzy inference system is trained by the neural network to obtain the least possible error between the desired output (target) and the fuzzy inference system (FIS) output to get the final FIS. The performance of the proposed speech recognition system obtaining a recognition ratio about 94.5%.