Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection


  • Julián Arias-Londoño Universidad Nacional de Colombia, Sede Manizales
  • Juan Godino-Llorente Universidad Politécnica de Madrid
  • Jorge Jaramillo-Garzón Universidad Nacional de Colombia, Sede Manizales
  • Germán Castellanos-Domínguez Universidad Nacional de Colombia, Sede Manizales


Nonlinear analysis of pathological voices, embedding spaces, hidden Markov models, dissimilarity space classification


This paper investigates a new way for modelling the nonlinear behavior present in pathological voice signals. The main idea is modelling the timedelay reconstructed attractors, taking into account the spatial and temporal information of the trajectories by means of a discrete Hidden Markov model (HMM). When the attractors are modeled with HMM it is possible to compute a probabilistic kernel-based distance among models to construct a dissimilarity space. This approach enables the possibility of comparing attractor families by their profiles, rather than evaluating individual nonlinear features of each subject. Classification of dissimilarity space is carried out by using a naive 1-nearest neighbors rule and it is compared with another classification scheme that employs two conventional nonlinear statistics: largest Lyapunov exponent and correlation dimension. Results show that the maximum accuracy with the proposed scheme is a 18.71% greater than the maximum accuracy obtained from the classification based on the conventional nonlinear statistics.
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How to Cite

Arias-Londoño, J., Godino-Llorente, J., Jaramillo-Garzón, J., & Castellanos-Domínguez, G. (2013). Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection. Revista Facultad De Ingeniería Universidad De Antioquia, (50), 111–121. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/14937

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