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Nonlinear Dynamics for Hypernasality Detection in Spanish Vowels and Words

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Abstract

A novel technique for characterizing hypernasal vowels and words using nonlinear dynamics is presented considering different complexity measures that are mainly based on the analysis of the time-delay embedded space. After the characterization stage, feature selection is performed by means of two different strategies: principal components analysis and sequential floating feature selection. The final decision about the presence or absence of hypernasality is carried out using a Soft Margin-Support Vector Machine. The database used in the study is composed of the five Spanish vowels uttered by 266 children, 110 healthy and 156 labeled as hypernasal by a experienced voice therapist. The database also includes the words /coco/ and /gato/ uttered by 119 children; 65 of which were diagnosed as hypernasal and the rest 54 as healthy. The results are presented in terms of accuracy, sensitivity and specificity. ROC curves are also included as a widely accepted way to measure the performance of a detection system. The experiments show that the proposed methodology achieves an accuracy of up to 92.08 % using, together, the best subset of features extracted from every vowel and 89.09 % using the combination of the most relevant features in the case of words.

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References

  1. Henningsson GE, Isberg AM. Velopharyngeal movement patterns in patients alternating between oral and glottal articulation: a clinical and cineradiographical study citation. Cleft Palate J. 1986;23(1):1–9.

    PubMed  CAS  Google Scholar 

  2. Kummer AW. Cleft palate and craniofacial anomalies: effects on speech and resonance. 2nd ed. Stamford: Cengage Learning; 2007.

    Google Scholar 

  3. Murillo-Rendón S, Orozco-Arroyave JR, Vargas-Bonilla JF, Arias-Londoño JD, Castellanos-Domínguez CG. “Automatic detection of hypernasality in children”, new challenges on bioinspired applications, vol 6687. Berlin: Springer; 2011. p. 167–174.

  4. Maier A, Hönig F, Hacker C, Shuster M, Nöth E. Automatic evaluation of characteristic speech disorders in children with cleft lip and palate. In: 11th international conference on spoken language processing, Brisbane-Australia; 2008. p. 1757–1760.

  5. Golding KK. therapy techniques for cleft palate speech and related disorders. San Diego: Singular Thomson Learning [Ed]; 2001.

    Google Scholar 

  6. Giovanni A, Ouaknine M, Guelfucci R, Yu T, Zanaret M, Triglia JM. Nonlinear behavior of vocal fold vibration: the role of coupling between the vocal folds. J Voice. 1999;13(4):456–476.

    Google Scholar 

  7. Orozco-Arroyave JR, Murillo-Rendón S, Álvarez-Meza A, Arias-Londoño JD, Delgado-Trejos E, Vargas-Bonilla JF, Castellanos-Domínguez CG. Automatic selection of acoustic and non-linear dynamic features in voice signals for hypernasality detection. In: Proceedings of Interspeech. 2011. p. 529–532.

  8. Henriquez P, Alonso JB, Ferrer MA, Travieso CM, Godino-Llorente JI, Díaz-de-María F. Characterization of healthy and pathological voice through measures based on nonlinear dynamics. IEEE Trans Audio Speech Lang Process. 2009;17(6):1186–1195.

    Article  Google Scholar 

  9. Delgado-Trejos E, Sepúlveda FA, Röthlisberger S, Castellanos-Domínguez G. The Rademacher complexity model over acoustic features for improving robustness in hypernasal speech detection. In: Computers and simulation in modern science, vol V. UK: WSEAS Press, University of Cambridge; 2011. p.130–135.

  10. Arias-Londoño JD, Godino-Llorente JI, Sáenz-Lechón N, Osma-Ruíz V, Castellanos-Domínguez G. Automatic detection of pathological voices using complexity measures, noise parameters and mel-cepstral coefficients. IEEE Trans Biomed Eng. 2011;58(2):370–379.

    Article  PubMed  Google Scholar 

  11. Lewis KE, Watterson T, Quint T. The effect of vowels on nasalance scores. Cleft Palate Craniofac J. 2000;37(6):584–589.

    Article  CAS  Google Scholar 

  12. Kuehn DP, Moller KT. Speech and language issues in the cleft palate population: the state of the art. Cleft Palate Craniofac J. 2000;37(4):1–35.

    Google Scholar 

  13. Titze IR. Workshop on acoustic voice analysis: summary statement. National Center for Voice and Speech, Denver, 1994.

  14. Jiang J, Zhang Y, McGilligan C. Chaos in voice, from modeling to measurement. J Voice. 2006;20(1):2–17.

    Article  PubMed  Google Scholar 

  15. Takens F. Detecting strange attractors in turbulence. Dynamical systems and turbulence: lecture notes in mathematics, vol 898. Springer: Berlin; 1981. p. 366–381.

  16. Kennel MB, Brown R, Abarbanel HDI. Determining embedding dimension for phase-space reconstruction using geometrical construction. Phys Rev A. 1992;45(6):3403-3411.

    Article  PubMed  Google Scholar 

  17. Fraser AM, Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A. 1986;33(2):1134–1140.

    Article  PubMed  Google Scholar 

  18. Shaheen A, Roy N, Jiang JJ. Nonlinear dynamic analysis of disordered voice: the relationship between the correlation dimension (D2) and pre-/post-treatment change in perceived dysphonia severity. J Voice. 2010;24(3):285–293.

    Article  Google Scholar 

  19. Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica D. 1983;9:189–208.

    Article  Google Scholar 

  20. Abarbanel HDI. Analysis of observed chaotic data, 1st ed. Inst. for Nonlinear Science. Springer: New York; 1996.

  21. Rosenstein MT, Collins JJ, De Luca CJ. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D. 1993;65:117–134.

    Article  Google Scholar 

  22. Oseledec VA. A multiplicative ergodic theorem. Lyapunov characteristic numbers for dynamical systems. Trans Moscow Math Soc. 1968;19:197–231.

    Google Scholar 

  23. Hurst HE, Black RP, Simaika YM. Long-term storage: an experimental study. 1st ed. London: Constable; 1965.

    Google Scholar 

  24. Kaspar F, Shuster HG. Easily calculable measure for complexity of spatiotemporal patterns. Phys Rev A. 1987;36(2):842–848.

    Article  PubMed  Google Scholar 

  25. Jolliffe IT. Principal Component Analysis, 2nd Ed. Springer series in statistics. Springer: New York; 2002.

  26. Daza-Santacoloma G, Arias-Londoño JD, Godino-Llorente JI, Sáenz-Lechón N, Osma-Ruíz V, Castellanos-Domínguez G. Dynamic feature extraction: an application to voice pathology detection. Intell Autom Soft Comput 2009;15(4):667–682.

    Google Scholar 

  27. Pudil P, Novovicova J, Kittler J. Floating search methods in feature selection. Patt Recogn Lett 1994;15(11):1119–1125.

    Article  Google Scholar 

  28. Scholköpf B, Smola AJ. Learning with Kernels. The MIT Press: Cambridge; 2002.

    Google Scholar 

  29. Lee GS, Wang CP, Fu S. Evaluation of hypernasality in vowels using voice low tone to high tone ratio. Cleft Palate J 2009;23(1):47–52.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was funded by ARTICA, financed by COLCIENCIAS and Ministerio de TIC in Colombia, project \(\hbox{N}^{\deg}\)1115-470-22055, in association with the Clínica Noel, Medellín. Also by CODI at Universidad de Antioquia, project MC11-1-03, by “Convocatoria 528 para estudios de doctorado en Colombia, generación del bicentenario, (COLCIENCIAS 2011)” and the project 2010238-PI/UAN-2011-473bit from Universidad Antonio Nariño, Colombia.

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Authors and Affiliations

  1. DIEyT, Universidad de Antioquia, Medellín, Colombia

    J. R. Orozco-Arroyave & J. F. Vargas-Bonilla

  2. FIEyB, Universidad Antonio Nariño, Bogotá, Colombia

    J. D. Arias-Londoño

  3. DIEEyC, Universidad Nacional de Colombia, Manizales, Colombia

    S. Murillo-Rendón & G. Castellanos-Domínguez

  4. Grupo de LPH, Fundación Clínica Noel, Medellín, Colombia

    J. F. Garcés

Authors
  1. J. R. Orozco-Arroyave
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  2. J. F. Vargas-Bonilla
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  3. J. D. Arias-Londoño
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  4. S. Murillo-Rendón
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  5. G. Castellanos-Domínguez
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  6. J. F. Garcés
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Correspondence to J. R. Orozco-Arroyave.

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Orozco-Arroyave, J.R., Vargas-Bonilla, J.F., Arias-Londoño, J.D. et al. Nonlinear Dynamics for Hypernasality Detection in Spanish Vowels and Words. Cogn Comput 5, 448–457 (2013). https://doi.org/10.1007/s12559-012-9166-z

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  • DOI: https://doi.org/10.1007/s12559-012-9166-z

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