KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Diagnosing Vocal Disorders using Cobweb Clustering of the Jitter, Shimmer, and Harmonics-to-Noise Ratio

  • Lee, Keonsoo (Medical Information Communication Technology, Soonchunhyang University) ;
  • Moon, Chanki (Department of Computer Science and Engineering Soonchunhyang University) ;
  • Nam, Yunyoung (Department of Computer Science and Engineering Soonchunhyang University)
  • Received : 2018.04.10
  • Accepted : 2018.05.30
  • Published : 2018.11.30
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Abstract

A voice is one of the most significant non-verbal elements for communication. Disorders in vocal organs, or habitual muscular setting for articulatory cause vocal disorders. Therefore, by analyzing the vocal disorders, it is possible to predicate vocal diseases. In this paper, a method of predicting vocal disorders using the jitter, shimmer, and harmonics-to-noise ratio (HNR) extracted from vocal records is proposed. In order to extract jitter, shimmer, and HNR, one-second's voice signals are recorded in 44.1khz. In an experiment, 151 voice records are collected. The collected data set is clustered using cobweb clustering method. 21 classes with 12 leaves are resulted from the data set. According to the semantics of jitter, shimmer, and HNR, the class whose centroid has lowest jitter and shimmer, and highest HNR becomes the normal vocal group. The risk of vocal disorders can be predicted by measuring the distance and direction between the centroids.

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References

  1. Williamson, G. Human Communication: A Linguistic Introduction. (Speechmark, 2001).
  2. M. Tiwari, and M. Tiwari. "Voice - How humans communicate?" J Nat Sci Biol Med 3, 3-11. 2012. https://doi.org/10.4103/0976-9668.95933
  3. Rose, P., "Forensic Speaker Identification," CRC Press, 2003.
  4. E. Keller, "The Analysis of Voice Quality in Speech Processing," Nonlinear Speech Modeling and Applications 54-73 Springer, Berlin, Heidelberg, 2005.
  5. J. D. Laver, "Voice quality and indexical information," Br J Disord Commun 3, 43-54. 1968. https://doi.org/10.3109/13682826809011440
  6. J. P. Teixeira, and P. O. Fernandes, "Jitter, Shimmer and HNR Classification within Gender, Tones and Vowels in Healthy Voices," Procedia Technology 16, 1228-1237. 2014. https://doi.org/10.1016/j.protcy.2014.10.138
  7. P. J. Murphy, "Spectral characterization of jitter, shimmer, and additive noise in synthetically generated voice signals," The Journal of the Acoustical Society of America 107, 978-988. 2000. https://doi.org/10.1121/1.428272
  8. J. P. Teixeira, C. Oliveira, and C. Lopes, "Vocal Acoustic Analysis - Jitter, Shimmer and HNR Parameters," Procedia Technology 9, 1112-1122. 2013. https://doi.org/10.1016/j.protcy.2013.12.124
  9. I. Smits, P. Ceuppens, andM. S. D. Bodt, "A Comparative Study of Acoustic Voice Measurements by Means of Dr. Speech and Computerized Speech Lab," Journal of Voice 19, 187-196. 2005. https://doi.org/10.1016/j.jvoice.2004.03.004
  10. F. B. Nunez, R. M. Gonzalez, M. G. Pelaez, I. L. Gonzalez, M. F. Fernandez, and M. G. Morato, "Acoustic voice analysis using the Praat program: comparative study with the Dr. Speech program," Acta Otorrinolaringol Esp 65, 170-176, 2014. https://doi.org/10.1016/j.otorri.2013.12.004
  11. H. Oguz, M. A. Kilic, and M. A. Safak, "Comparison of results in two acoustic analysis programs: Praat and MDVP," Turk J Med Sci 41, 835-841, 2011.
  12. Vogel, A. P. & Maruff, P. "Comparison of voice acquisition methodologies in speech research," Behavior Research Methods 40, 982-987. 2008. https://doi.org/10.3758/BRM.40.4.982
  13. A. Lovato, W.D. Colle, L. Giacomelli, A. Piacente, L. Righetto, G. Marioni, C. Filippis, "Multi-Dimensional Voice Program (MDVP) vs Praat for Assessing Euphonic Subjects: A Preliminary Study on the Gender-discriminating Power of Acoustic Analysis Software," Journal of Voice 30, 765.e1-765.e5. 2016. https://doi.org/10.1016/j.jvoice.2015.10.012
  14. G. Biswas, J. B. Weinberg, and D. H. Fisher, "ITERATE: a conceptual clustering algorithm for data mining," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 28, 219-230. 1998.
  15. D. H. Fisher, "Knowledge Acquisition Via Incremental Conceptual Clustering," Machine Learning 2, 139-172. 1987.
  16. M.K. Christmann, A.R. Brancalioni, C.R. Freitas, D.Z. Vargas, M. Keske-Soares, C.L. Mezzomo, and H.B. Mota, "Use of the program MDVP in different contexts: a literature review," Revista CEFAC 17, 1341-1349. 2015. https://doi.org/10.1590/1982-021620151742914
  17. P. Campisi, T. L. Tewfik, J. J. Manoukian, M. D. Schloss, E. Pelland-Blais, and N. Sadeghi, "Computer-Assisted Voice Analysis: Establishing a Pediatric Database," Arch Otolaryngol Head Neck Surg, vol. 128, no. 2, pp. 156-160, Feb. 2002. https://doi.org/10.1001/archotol.128.2.156
  18. "Dr. Speech Software." [Online]. Available: . [Accessed: 25-Feb-2018].
  19. "Praat: doing Phonetics by Computer." [Online]. Available: . [Accessed: 25-Feb-2018].
  20. "CSpeech Analysis Software." [Online]. Available: . [Accessed: 25-Feb-2018].
  21. KayPentax. Software instruction manual: Multi-Dimensional Voice Program(MDVP) Model 5105. (KayPentax, 2008).
  22. A. K. Jain, J. Mao, and K. M. Mohiuddin, "Artificial neural networks: a tutorial". Computer 29, 31-44. 1996.
  23. M. M. Adankon, and M. Cheriet, "Support Vector Machine," Encyclopedia of Biometrics, 1303-1308. Springer, Boston, MA, 2009.
  24. S. R. Safavian, and D. Landgrebe, "A survey of decision tree classifier methodology," IEEE Transactions on Systems, Man, and Cybernetics 21, 660-674 1991. https://doi.org/10.1109/21.97458
  25. M.-L. Zhang, and Z.-H. Zhou, "A k-nearest neighbor based algorithm for multi-label classification," in Proc. of 2005 IEEE International Conference on Granular Computing 2, 718-721 Vol. 2. 2005.
  26. T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: A New Data Clustering Algorithm and Its Applications," Data Mining and Knowledge Discovery 1, 141-182. 1997. https://doi.org/10.1023/A:1009783824328
  27. P. J. Grother, G. T. Candela, and J. L. Blue, "Fast implementations of nearest neighbor classifiers," Pattern Recognition 30, 459-465. 1997. https://doi.org/10.1016/S0031-3203(96)00098-2

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