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Pathological voice discrimination using cepstral analysis, vector quantization and Hidden Markov Models

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3 Author(s)
Costa, S.C. ; Fed. Center of Technol. Educ. of Paraiba, Fed. Univ. of Campina Grande, Campina Grande ; Aguiar Neto, B.G. ; Fechine, J.M.

Pathological voice discrimination has been made using digital signal processing techniques as a complementary tool to videolaringoscopy exams. This method is non-invasive to patients compared to laringoscopy. This paper aims at analyzing the use of cepstral analysis to discriminate voices affected by vocal fold pathologies. A Vector Quantizer using a distortion measurement followed by a Hidden Markov Model-based classifier is employed. Results obtained show an effective and objective way in analyzing voice disorders caused by a vocal fold pathology.

Published in:

BioInformatics and BioEngineering, 2008. BIBE 2008. 8th IEEE International Conference on

Date of Conference:

8-10 Oct. 2008