Speaker identification using FrFT-based spectrogram and RBF neural network

This paper address a speaker identification problem using optimized spectrogram and radial basis function (RBF) neural network. The proposed approach applies fractional Fourier transform (FrFT) to obtain spectrograms with different orders, which gives much more refined description of the speech signals. To reduce the computational complexity, these spectrograms are converted into low-dimensional vectors by local binary patterns (LBP) operator. The LBP vectors compose the searching space of particle swarm optimization (PSO) algorithm which is designed for find the optimal spectrogram. The fitness function of PSO algorithm is designed by between-class distances and within-class distances. Through getting the optimal LBP vectors, the similarity criterion is used to find the fractional orders corresponding to the optimal spectrograms. Then, the optimal speech features are fed to the RBF network for training and testing. The numerical experiments indicate that our approach has an acceptable recognition rate with high accuracy.