Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection
DOI:
https://doi.org/10.17533/udea.redin.14937Palabras clave:
Nonlinear analysis of pathological voices, embedding spaces, hidden Markov models, dissimilarity space classificationResumen
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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