Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44083
Title: Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics
Authors: Henriquez Rodriguez, Patricia 
Alonso Hernández, Jesús Bernardino 
Ferrer Ballester, Miguel Ángel 
Travieso González, Carlos Manuel 
Godino Llorente, Juan Ignacio
Díaz-de-María, Fernando
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Pathology , Databases , Entropy , Speech , Linear approximation , Chaos , Phase measurement , Mutual information , Measurement standards , Neural networks , Chaos , disordered speech , entropy , nonlinearity
Issue Date: 2009
Publisher: 1558-7916
Journal: IEEE Transactions on Audio, Speech and Language Processing 
Abstract: In this paper, we propose to quantify the quality of the recorded voice through objective nonlinear measures. Quantification of speech signal quality has been traditionally carried out with linear techniques since the classical model of voice production is a linear approximation. Nevertheless, nonlinear behaviors in the voice production process have been shown. This paper studies the usefulness of six nonlinear chaotic measures based on nonlinear dynamics theory in the discrimination between two levels of voice quality: healthy and pathological. The studied measures are first- and second-order Renyi entropies, the correlation entropy and the correlation dimension. These measures were obtained from the speech signal in the phase-space domain. The values of the first minimum of mutual information function and Shannon entropy were also studied. Two databases were used to assess the usefulness of the measures: a multiquality database composed of four levels of voice quality (healthy voice and three levels of pathological voice); and a commercial database (MEEI Voice Disorders) composed of two levels of voice quality (healthy and pathological voices). A classifier based on standard neural networks was implemented in order to evaluate the measures proposed. Global success rates of 82.47% (multiquality database) and 99.69% (commercial database) were obtained.
URI: http://hdl.handle.net/10553/44083
ISSN: 1558-7916
DOI: 10.1109/TASL.2009.2016734
Source: IEEE Transactions on Audio, Speech and Language Processing[ISSN 1558-7916],v. 17, p. 1186-1195
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