Implementation of speaker verification system using Fuzzy Wavelet Network

A Fuzzy Wavelet network (FWN) is proposed to model the characteristics of a speaker in an automatic speaker verification system in this paper. The neural network using wavelet as activation function is wavelet network (Wavenet). Wavenet has the ability to extract the distinguishable and essential features in frequency rich signals. This is required in classification and identification problems such as speaker verification. Nonlinearity and structured knowledge representation with human perception of fuzzy inference system makes it to be a suitable model for speaker verification when combined with the wavelet network. In this approach, the wavelet theory is combined with the fuzzy based neural network theory which leads to construction of Fuzzy Wavelet Network (FWN). The advantage of fuzzy wavelet network is that the membership functions can be easily merged or divided using the multi resolution properties and the rules can be evaluated during learning. The performance of the proposed speaker verification system is evaluated with TIMIT database. A comparison is made between the proposed system and the system using state of the art model (GMM). Compared with GMM and WNN, FWN provides better verification performance.