Adaptive Network Based Fuzzy Inference System For Speech Recognition Through Subtractive Clustering

Abstract

Fuzzy modeling require two main steps which are structure identification and parameter optimization, the first one determines the numbers of membership functions and fuzzy if-then rules, while the second identifies a feasible set of parameters under the given structure. However, the increase of input dimension, rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system. First learning of the network structure by subtractive clustering, in order to define an optimal structure and obtain small number of rules, then learning of parameters network by hybrid learning which combine the gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents parameters. The results obtained show the effectiveness of the method in terms of recognition rate and number of fuzzy rules generated.