Speaker verification using Mel Frequency Cepstral Coefficient and Artificial Neural Network

Abstract

Speaker recognition is defined as to make sure that if the person is the same person he claims to be or not. This technique is one of the biometric recognition techniques useful in all most all areas where security is a concern.
Speaker recognition can be divided into speaker identification and speaker verification. Speaker identification decides if a speaker is a specific person or is from a group. In speaker verification, a person makes an identity claim (e.g., by entering a pin number with the debit/credit card at ATM).
There are two main stages in this technique, feature extraction and feature matching. Feature extraction is the process in which we extract some useful data which can later to be used to represent the speaker. Feature matching involves identification of the unknown speaker by comparing the feature extracted from the voice with the enrolled voices of known speakers.
In this project we have extracted the MFCCs of the speech signal, which involve recording of the speech signal, windowing, framing, thresholding, STDFT (short time discrete fourier transform) calculation and then passing through mel frequency filter. Extracted features are then matched with the stored templates. Algorithms used in feature extraction are calculation of real cepstral coefficient calculation and mel frequency cepstral coefficient calculation. For feature matching we used multi-layer perceptron method in artificial neural network.