On the effectiveness of MFCCs and their statistical distribution properties in speaker identification

This paper presents a study on the effectiveness of mel-frequency cepstrum coefficients (MFCCs) and some of their statistical distribution properties (skewness, kurtosis, standard deviation) as the features for text-dependent speaker identification. Multi-layer neural network with backpropagation learning algorithm is used here as the classification tool. The MFCCs representing the speaker characteristics of a speech segment are computed by nonlinear filterbank analysis and discrete cosine transform. The speaker identification efficiency and the convergence speed of the neural network are investigated for different combinations of the proposed features. The result shows that the first MFCC degrades the identification competence and statistical distribution parameters enhance the training speed of the neural network.