SPEECH RECOGNITION USING A WAVELET TRANSFORM TO ESTABLISH FUZZY INFERENCE SYSTEM THROUGH SUBTRACTIVE CLUSTERING AND NEURAL NETWORK (ANFIS)

In this paper, a proposed speech recognition algorithm is presented. This paper deals with the combination of a feature extraction by wavelet transform, subtractive clustering and adaptive neuro-fuzzy inference system (ANFIS). 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 algorithm (SRA) using a wavelet transform and ANFIS is evaluated by different samples of speech signals- isolated words- with added background noise. The proposed speech recognition algorithm is tested using different isolated words obtaining a recognition ratio about 99%.