Filter Bank Feature Extraction for Gaussian Mixture Model Speaker Recognition

Speaker Recognition is the task of identifying an individual from their voice. Typically this task is performed in two consecutive stages: feature extraction and classification. Using a Gaussian Mixture Model (GMM) classifier different filter-bank configurations were compared as feature extraction techniques for speaker recognition. The filter-banks were also compared to the popular Mel-Frequency Cepstral Coefficients (MFCC) with respect to speaker recognition performance on the CSLU Speaker Recognition Corpus. The empirical results show that a uniform filter-bank outperforms both the mel-scale filter bank and the MFCC as a feature extraction technique. These results challenge the notion that the mel-scale is an appropriate division of the spectrum for speaker recognition.