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

Authors

  • 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

Keywords:

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

Abstract


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.
|Abstract
= 10 veces | PDF (ESPAÑOL (ESPAÑA))
= 8 veces|

Downloads

Download data is not yet available.

References

J. J. Jiang, Y. Zhang, C. McGilligan. “Chaos in voice, from modeling to measurement,” Journal of Voice. Vol. 20. 2006. pp. 2-17.

Y. Zhang, J. Jiang, L. Biazzo, M. Jorgensen. “Perturbation and nonlinear dynamic analysis of voices from patients with laryngeal paralysis,” Journal of Voice. Vol. 19. 2004. pp. 519-528.

Y. Zhang, C. McGilligan, L. Zhou, M. Vig, J. Jiang. “Nonlinear dynamic analysis of voices before and after surgical excision of vocal polyps.” Journal of the Acoustical Society of America. Vol. 115. 2008. pp. 2270-2277.

J. I. Godino-Llorente, P. Gómez-Vilda, M. Blanco- Velasco. “Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters”. IEEE Transactions on Biomedical Engineering. Vol. 53. 2006. pp. 1943-1953.

N. Sáenz-Lechón, J. I.Godino-Llorente, V. Osma- Ruiz, P. Gómez-Vilda. “Methodological issues in the development of automatic systems for voice pathology detection”. Biomedical Signal Processing and Control.Vol.1. 2006. pp. 120-128.

I. R. Titze, R. Baken, H. Herzel. “Evidence of chaos in vocal fold vibration”. Vocal Fold Physiology: New Frontiers in Basic Science. Singular Publishing Group. San Diego. CA. 1993. pp 143-188.

M. A. Little. P. E. McSharry. S. J. Roberts, D. A. Costello, I. M. Moroz. “Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection”. Biomedical Engineering Online. Vol. 6. 2007. pp. 1-35.

H. Kantz, T.Schreiber. Nonlinear time series analysis, 2a ed., Cambridge University Press. Cambridge. UK. 2003.

M. C. Scharry, “Detection of dynamical transitions in biomedical signals using nonlinear methods,” Proceedings of 8th International Conference KES, Lecture Notes in Computer Science. Ed. Springer. Wellington. New Zeland. Vol. 3215. 2004. pp. 483- 490.

J. S. Richman, J. R. Moorman. “Physiological timeseries analysis using approximate entropy and sample entropy”. Am J Physiol HeartCirc Physiol. Vol. 278. 2000. pp. H2039-H2049.

T. Jebara, R. Kondor, A. Howard. “Probabilistic product kernels”. Journal of Machine Learning Research. Vol. 5. 2004. pp. 819-844.

A. Giovanni, M. Ouaknine, J. M. Triglia. “Determination of largest lyapunov exponents of vocal signal: Application to unilateral laryngeal paralysis”. Journal of Voice. Vol. 13. 1999. pp. 341-454.

Massachusetts Eye and Ear Infirmary. Voice disorders database. version 1.03. [CD-ROM]. 1994. Lincoln Park. N.J. Kay Elemetrics Corp.

O. Cappé, E. Moulines, T. Rydén. Inference in Hidden Markov Models. Ed. Springer. New York. 2005. pp. 1-654.

L. Chen, H. Man. “Fast schemes for computing similarities between Gaussian HMMs and their applications in texture image classification,” EURASIP Journal on Applied Signal Processing. Vol. 13. 2005. pp. 1984-1993.

E. Pekalska, R. Duin. “Dissimilarity representations allow for building good classifiers,” Pattern Recognition Letters. Vol. 23. 2002. pp 943-956.

E. Pekalska, R. Duin, P. Placík. “Prototype selection for dissimilarity-based classifiers” Pattern Recognition. Vol. 39. 2006. pp. 189-208.

R.O. Duda, P. E.Hart, D. G. Stork. Pattern Classification. 2a ed. Ed. Wiley Interscience. New York. 2000.

V. Parsa, D.Jamieson. “Identification of pathological voices using glottal noise measures.” Journal of Speech, Language and Hearing Research. Vol. 43. 2000. pp. 469-485.

M. Bicego, V. Murino, M. Figueiredo, “Similaritybased classification of sequences using Hidden Markov Models”. Pattern Recognition. Vol 37. 2004. pp. 2281-2291.

M. Small, Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance. Ed. World Scientific. Singapore. 2005. pp. 1-245.

Published

2013-03-20

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
w

Most read articles by the same author(s)