Abstract

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 …

Date

2002

Authors

Alireza A Dibazar, Shrikanth Narayanan, Theodore W Berger

Conference

Proceedings of the second joint 24th annual conference and the annual fall meeting of the biomedical engineering society][engineering in medicine and biology

Volume

1

Pages

182-183

Publisher

IEEE