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).