Section: New Results

Pathological Speech Analysis

Participants : Khalid Daoudi, Vahid Khanagha, Blaise Bertrac, Safa Mrad, Ashwini Jaya Kumar.

References: [14] , [13] , [26] , [27] .

We applied our recent results in nonlinear speech analysis to the filed of pathological speech detection and classification. We presented new insights in the task of normal-vs-pathological voice classification using the widely used Kayelemetrics database. In particular, we showed that hat one single parameter, derived from matching pursuit decomposition of speech, allow perfect discrimination between normal and dysphonic voices of these database. This result raises some important questions on the way this task is generally addressed. Using our GCI detection algorithm, we also proposed new definitions of standard voice perturbation measures (jitter, shimmer...) which lead to significantly higher classification accuracy. Our new measures have the strong advantage to avoid the usual periodicity and linearity assumptions. On the other hand, we started investigating the task of discrimination between Parkinson's and healthy voices. Our phonetic segmentation algorithm has potentially the ability to detect vowel onset and offset regions which have different structures in Parkinson's voices that in healthy ones. This preliminary result is promising and we are continuing research in this direction.