A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

Year 2018, Volume: 18 Issue: 1, 60 - 71, 23.02.2018

Haydar Ankışhan

Abstract

Voice data has
demonstrated chaotic behavior in previous studies. Therefore, studying the
linear properties alone does not yield successful results. This is valid for
the examination of voice data as well. Therefore, conducting studies including
chaotic features as well as existing technologies is inevitable. The main
purpose of this study is to detect voice pathologies with fewer special
features using new chaotic features. Both linear and nonlinear characteristics
were used in this study. In this context, the largest Lyapunov exponents and
entropy are preferred as chaotic properties because of their success in
previous studies. Very few results with 100% accuracy were obtained in the
experimental studies. In this study, multiple support vector machines (SVMs)
were selected as a classifier because of their success in previous similar data
types. Thus, the desired accuracy level was achieved using fewer features.
Resultantly, the process complexity decreased and the system speed increased.

Keywords

Voice disorders, support vector machines, voice disorder detection

References

• 1. J. R. O. Arroyave, J.F.V. Bonilla, E. D. Trejos, “Acoustic analysis and non linear dynamics applied to voice pathology detection: A review”, Recent Patents on Signal Processing, vol. 2, pp. 1–11, 2012. 2. S. Fong, K. Lan, R. Wong, “Classifying human voices by using hybrid SFX time-series preprocessing and ensemble feature selection”, BioMed Research International, vol. 1–27, 2013. 3. J. I. Godino-Llorente, N. S´aenz-Lech´on, V. Osma-Ruiz, S.Aguilera-Navarro, P. G´omez- Vilda, “An integrated tool for the diagnosis of voice disorders,” Medical Engineering & Physics, vol. 28, pp. 276–289, 2006 4. C. Manfredi, M. D’Aniello, P. Bruscaglioni, A. Ismaelli, “A comparative analysis of fundamental frequency estimation methods with application to pathological voices,” Medical Engineering & Physics, vol. 22, pp. 135–147, 2000. 5. M. Farrus, J. Hernando, P. Ejarque, “Jitter and shimmer measurements for speaker Recognition,” Annual Conference of the International Speech Communication Association (Interspeech 2007), Antwerp, Belgium, pp. 778–781, 2007. 6. P. Lieberman, “Some acoustic measures of the fundamental periodicity of normal and pathologic larynges”, The Journal of the Acoustical Society of America, vol. 35, pp. 344–353, 1963. 7. Y. Horii, “Vocal shimmer in sustained phonation,” Journal of Speech, Language, and Hearing Research, vol. 23, pp. 202–209, 1980. 8. E. Yumoto, W. J. Gould, T. Baer, “Harmonics-to-noise ratio as an index of the degree of hoarseness,” The Journal of the Acoustical Society of America, vol. 71, pp: 1544–1550, 1982. 9. L. R. Rabiner and B. H. Juang, “Fundamentals of Speech Recognition,” Vol. 14, PTR Prentice Hall, Englewood Cliffs, NJ. 10. J. I. Godino-Llorente, P. Gomez-Vilda “Automatic detection of voice impairmentsby means of short-term cepstral parameters and neural network based detectors,” IEEE Trans. Biomed. Eng., vol. 51, pp. 380–384, 2004. 11. Y. Zhang, et al. “Nonlinear dynamic analysis of voices before and after surgicalexcision of vocal polyps,” J. Acoust. Soc. Am., vol. 115, pp. 2270–2277, 2004. 12. T. L. Eadie, P. C. Doyle, “Classification of dysphonic voice: Acoustic and auditory-perceptual measures,” Journal of Voice, vol. 19, pp. 1–14, 2005. 13. S. Hadjitodorov and P. Mitev, “A computer system for acoustic analysis of pathological voices and laryngeal diseases screening,” Medical Engineering & Physics, vol. 24, p. 419–429, 2002. 14. N. Saenz-Lechon, J. I. Godino-Llorente, V. Osma-Ruiz, M. Blanco-Velasco, F. Cruz-Roldan, “Automatic assessment of voice quality according to the GRBAS scale,” 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’06, New York, NY, USA, pp. 2478–2481, 2006. 15. K. Umapathy, S. Krishnan, V. Parsa, D. G. Jamieson, “Discrimination of pathological voices using a time-frequency approach,” IEEE Transactions on Biomedical Engineering, vol. 52, p. 421–430, 2005. 16. J. I. Godino-Llorente, P. Gomez-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, p. 1943–1953, 2006. 17. S. Jothilakshmi, “Automatic system to detect the type of voice pathology,” Applied Soft Computing, 2014. 18. A. Skalski, T. Zielinski, D. Deliyski, “Analysis of vocal folds movement in high speed videoendoscopy based on level set segmentation and image registration,” International Conference on Signals and Electronic Systems, ICSES’ 08, Krak´ow, Poland, pp. 223–226, 2008. 19. D. D. Mehta, D. D. Deliyski, S. M. Zeitels, T. F. Quatieri, R. E. Hillman, “Voice production mechanisms following phonosurgical treatment of early glottic cancer,” The Annals of Otology, Rhinology, and Laryngology, vol. 119, p. 1, 2010. 20. R. Srinivasan, R. Rengaswamy, R. Miller, “A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops,” Control Engineering Practice vol. 15, pp. 1135-1148, September, 2007. 21. J. C. Saldanha, T. Ananthakrishna, R. Pinto, “Vocal fold pathology assessment using mel- frequency cepstral coefficients and linear predictive cepstral coefficients features,” Journal of Medical Imaging and Health Informatics, vol. 4, pp. 168–173, 2014. 22. L. Deng and O. Douglas O. “Speech processing: a dynamic and optimization-oriented approach,” Marcel Dekker, pp. 41–48, 2003. 23. N. Levinson, “The Wiener RMS error criterion in filter design and prediction,” J. Math. Phys., vol. 25, pp. 261–278, 1947. 24. M. Xu, et al. “HMM-based audio keyword generation,” In Kiyoharu Aizawa; Yuichi Nakamura; Shin’ichi Satoh. Advances in Multimedia Information Processing – PCM 2004: 5th Pacific Rim Conference on Multimedia (PDF). Springer. 25. Takens F. “Detecting strange attractors in turbulence”, Dynamical Systemsand Turbulence, Warwick 1980, Springer, 1981, pp. 366–381. 26. N. L. Johnson, S. Kotz, N. Balakrishnan “Continuous Univariate Distributions,” 1994, vol.1, 2nd Edition Wiley. 27. P. H. Westfall, “ Kurtosis as Peakedness,” 1905–2014. R.I.P., The American Statistician 68, 2014, p. 191–195. 28. C. E. Shannon, “A mathematical theory of communication”, Bell System Technical Journal, vol. 27, pp. 623–656, 1948. 29. M. T. Rosenstein, J. J. Collins, C. J. De Luca. “A practical method for calculating largest Lyapunov exponents from small data sets,” Physica D, vol. 65, pp. 117–134, 1993. 30. V. Vapnik. “Statistical Learning Theory”, New York, NY, USA: John Wiley&Sons, 1998. 31. T. Joachims, “Making large-scale SVM learning practical in Advances in Kernel Methods-Support Vector Learning”, B. Schlkopf, C. J. C. Burges, and A. J. Smola, Eds., 1999, pp. 169–184, MIT Press, Cambridge, Mass, USA. 32. H. Ankışhan and D. Yılmaz, “Comparison of SVMs and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponents,” Computational and Mathematical Methods in Medicine, vol. 2013, 2013. 33. C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transaction on Neural Networks, vol. 13, pp. 415-426, May 2002. 34. G. Hamzeh, T. K. Mehdi, K. A. Meisam, P. Mohammad “Detection of vocal disorders based on phase space parameters and Lyapunov spectrum,” Biomedical Signal Processing and Control, vol. 22, p. 135–145, 2015. 35. A. Akbari, M. K. Arjmandi, “Employing linear prediction residual signal of waveletsub-bands in automatic detection of laryngeal pathology,” Biomed. Signal Processing and Control vol. 18, 293–302, 2015. 36. M. K. Arjmandi, M. Pooyan, “An optimum algorithm in pathological voice qualityassessment using wavelet-packet-based features, linear discriminant analysisand support vector machine,” Biomedical Signal Processing and Control, vol. 7, pp. 3–19, 2012. 37. M. K. Arjmandi, et al. “Identification of voice disorders using long-time featuresand support vector machine with different feature reduction methods,” J. Voice, vol. 25, pp. 275–289, 2011. 38. J. D. Arias-Londono, et al. “Automatic detection of pathological voices using com-plexity measures, noise parameters, and mel-cepstral coefficients,” IEEE Trans. Biomed. Eng. vol. 58, pp. 370–379, 2011.