Comparison of neural networks for speaker recognition

In a world where authentication and privacy are taking a lot of our daily efforts, it is becoming more important for us to prove our identity to different systems every day so that we can access required and useful services. The problem addressed in this research is speaker verification as it involves knowing the identity of a given speaker using a predefined set of samples. The steps of this process start with processing the voice signal using the fast Fourier transform (FFT), the Hanning window, and a histogram representation to make it suitable for the next part. The identification part is based on a neural network where the identification can be done in one or two classification parts. Finally, several different algorithms were tested and the results compared