Principal Component Analysis of Spectral Perturbation Parameters for Voice Pathology Detection

Authors:
F. Diaz;A. Alvarez;K. Murphy;C. Lazaro
Affiliations:
Campus de Montegancedo;Campus de Montegancedo;Campus de Montegancedo;Campus de Montegancedo
Venue:
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Year:
2005

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Linear classifier with reject option for the detection of vocal fold paralysis and vocal fold edema

EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
An improved method for voice pathology detection by means of a HMM-based feature space transformation

Pattern Recognition

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Abstract

In recent years emphasis has been placed upon the early detection of voice pathologies by using the signal processing of voice to evaluate certain time and spectrum domain parameters which may infer the presence of pathology. The present work is aimed at establishing the suitability of these voice spectral parameters in fixing a clear distinction between pathologic and normophonic voice, and to further classify the specific patientýs pathology. Principal Component Analysis is used in parameter selection. Results for normal and pathological samples will be presented and discussed.