Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection
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| Format: | Artículo científico |
| Langue: | en |
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Universidad de Antioquia
2009
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| _version_ | 1876479121043750912 |
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| author | Julián Arias Londoño |
| author_facet | Julián Arias Londoño |
| contents | Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection Julián Arias Londoño Juan Godino Llorente Jorge Jaramillo Garzón Germán Castellanos Domínguez Ingeniería embedding spaces hidden Markov models dissimilarity space classification Nonlinear analysis of pathological voices This paper investigates a new way for modelling the nonlinear behavior present in athological 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. 2009 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43016338010 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.50 |
| format | Artículo científico |
| id | redalyc_43016338010 |
| institution | Redalyc |
| language | en |
| publishDate | 2009 |
| publisher | Universidad de Antioquia |
| spellingShingle | Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection Julián Arias Londoño Ingeniería embedding spaces hidden Markov models dissimilarity space classification Nonlinear analysis of pathological voices Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection Julián Arias Londoño Juan Godino Llorente Jorge Jaramillo Garzón Germán Castellanos Domínguez Ingeniería embedding spaces hidden Markov models dissimilarity space classification Nonlinear analysis of pathological voices This paper investigates a new way for modelling the nonlinear behavior present in athological 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. 2009 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43016338010 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.50 |
| title | Dissimilarity-based classification for stochastic models of embedding spaces applied to voice pathology detection |
| topic | Ingeniería embedding spaces hidden Markov models dissimilarity space classification Nonlinear analysis of pathological voices |
| url | https://www.redalyc.org/articulo.oa?id=43016338010 |