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Phoneme independent pathological voice detection using wavelet based MFCCs, GMM-SVM hybrid classifier

The paper proposes a new method for the phoneme independent normal and pathological voice classification. The new method proposes a wavelet sub band based hybrid classifier built by combining Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Support Vector Machine (SVM). The Mel Frequency Cepstral Coefficients (MFCCs) are computed for each sub band obtained by wavelet decomposition. The MFCCs of each sub band are modelled using GMM-UBM. Finally the scores of GMM-UBMs are fused using SVM. The fusion of GMM -UBM for wavelet sub band MFCCs and SVM gives a maximum accuracy of 96.61% whereas conventional MFCCs with GMM -UBM gives 85.18%.