A new approach based on Empirical Mode Decomposition for the analysis of voice features

MARÍA E. TORRES; GASTÓN SCHLOTTHAUER; HUGO L. RUFINER; MARÍA C. JACKSON-MENALDI

Dublín, Irlanda

Otro; International School 2009: "Development of Multimodal Interfaces: Active Listening and Synchrony"; 2009

European Science Foundation (SDF)

Empirical Mode Decomposition (EMD) is a data driven technique proposed by Huang. We explore properties if the intrinsic mode functions (IMFs) and present two applications to the analysis of voice. First we apply EMD to obtain new tools for discrimination between normal and pathological voices, applying it ti speech signals corresponding to real and simulated sustained vowels. Flor the synthetic sustained vowels we propose a phonation model that includes perturbations implied in common laryngeal pathologies. By means or standard spectral analysis of the IMFs, we extract features from each signal. Using a basic pattern classification algorithm, the spectral features of only three intrinsic mode functions are enough to discriminate between normal and pathological voices, with a data base of 106 different voices, half normal an d half of diverse pathologies. Additionally we consider two pathologies of different etiology and treatment, which, given the similarity of their voice characteristics, are frequently misdiagnosed in clinical practice: muscular real data base (33 pathological voices) suggest that approach could provide to the extraction of the voice fundamental frequency will be introduced. A new algorithm and preliminary results will be presented. Taking into account the relationship between this parameter and emotions.