Modélisation Statistique et Informations Pertinentes pour la Caractérisation
Gilles Pouchoulin, Corinne Fredouille, Jean-François Bonastre, Alain Ghio, Marion Azzarello.

This paper investigates the class of information relevant for the task of automatic classification of pathological voices. By using a GMM-based classification system (derived from the Automatic Speaker Recognition domain), the focus was made on three main classes of information : energetic, voiced, and phonetic information. Experiments made on a pathological corpus (dysphonia) have shown that phonetic information is particularly interesting in this context since it permits to refine the selection of the relevant information by looking at phonem- or phonem classlevel (e.g. nasal vowels).