Normalized modulation spectral features for cross-database voice pathology detection

In this paper, we employ normalized modulation spectral analysis for voice pathology detection. Such normalization is important when there is a mismatch between training and testing conditions, or in other words, employing the detection system in real (testing) conditions. Modulation spectra usually produce a high-dimensionality space. For classification purposes, the size of the original space is reduced using Higher Order Singular Value Decomposition (SVD). Further, we select most relevant features based on the mutual information between subjective voice quality and computed features, which leads to an adaptive to the classification task modulation spectra representation. For voice pathology detection, the adaptive modulation spectra is combined with an SVM classifier. To simulate the real testing conditions; one for training and the other for testing. We address the difference of signal characteristics between training and testing data through subband normalization of modulation spectral features. Simulations show that feature normalization enables the cross-database detection of pathological voices even when training and test data are different.