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Voice Pathology Detection and Discrimination Based on Modulation Spectral Features

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2 Author(s)
Markaki, M. ; Comput. Sci. Dept., Univ. of Crete, Heraklion, Greece ; Stylianou, Y.

In this paper, we explore the information provided by a joint acoustic and modulation frequency representation, referred to as modulation spectrum, for detection and discrimination of voice disorders. The initial representation is first transformed to a lower dimensional domain using higher order singular value decomposition (HOSVD). From this dimension-reduced representation a feature selection process is suggested using an information-theoretic criterion based on the mutual information between voice classes (i.e., normophonic/dysphonic) and features. To evaluate the suggested approach and representation, we conducted cross-validation experiments on a database of sustained vowel recordings from healthy and pathological voices, using support vector machines (SVMs) for classification. For voice pathology detection, the suggested approach achieved a classification accuracy of 94.1±0.28% (95% confidence interval), which is comparable to the accuracy achieved using cepstral-based features. However, for voice pathology classification the suggested approach significantly outperformed the performance of cepstral-based features.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:19 ,  Issue: 7 )

Date of Publication:

Sept. 2011

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