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Feature analysis for automatic detection of pathological speech

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3 Author(s)
A. A. Dibazar ; Dept. of Biomed. Eng., Univ. of Southern California, Los Angeles, CA, USA ; S. Narayanan ; T. W. Berger

This study focuses on a robust, rapid and accurate system for automatic detection of normal and pathological speech. This system employs noninvasive, non-expensive and fully automated measures of vocal tract characteristics and excitation information. Mel-frequency filterbank cepstral coefficients and measures of pitch dynamics were modeled by Gaussian mixtures in a hidden Markov model (HMM) classifier. The method was evaluated using the sustained phoneme /a/ data obtained from over 700 subjects of normal and different pathological cases from the Massachusetts Eye and Ear Infirmary (MEEI) database. This method attained 99.44% correct classification rates for discrimination of normal and pathological speech for sustained /a/. This represents 8% detection error rate improvement over the best performing classifier using carefully measured features prevalent in the state-of-the-art in pathological speech analysis.

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Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint  (Volume:1 )

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