Modelling Mutual Information between Voiceprint and Optimal Number of Mel-Frequency Cepstral Coefficients in Voice Discrimination

In this paper, we study the relationship between the voiceprint and the optimal number of Mel-frequency Cepstral Coefficients (MFCCs). The voiceprint is modelled as sub-MFCCs matrix with the first d number of MFCCs. We model the relationship through information theory and formulate it as the mutual information maximization problem subject to the probabilities constraint. The solution of this optimization problem provides the optimal number of MFCCs, D among these d, which yields the highest classification accuracy of the voice discrimination, together with a confidence level. This study is dictated by the need to understand the use of MFCCs, which have proliferated since its invention to discriminate voice. We evaluate our model by comparing the leave-one-out cross validation (LOOCV) results of usual multi-class classifier, the Supervised Learning Gaussian Mixture Model (SLGMM), with a set of spoken words and A capella solo vocal performances. The experimental results show that our model is a more comprehensive feature selection criteria for the MFCCs than the de-facto technique, LOOCV.