Abstract
A novel way for characterizing hypernasal voices by means of nonlinear dynamics is presented considering some complexity measures that are mainly based on the analysis of the embedding space. After characterization, feature selection is performed using two strategies, Principal Components Analysis (PCA) and Secuential Floating Feature Selection (SFFS); classification between healthy and hypernasal voices is carried out with a Soft Margin - Support Vector Machine (SM-SVM). 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 phoniatrics expert. The experimental results are presented in terms of accuracy, sensitivity and specificity to show in a quantitatively manner, how stable and reliable is the methodology. ROC curves are also included to present a widely accepted statistic for the accuracy of the system. According to the results, nonlinear dynamic theory is able to detect hypernasal voices, and would be worth to continue developing this kind of studies oriented to automatic detection of pathological voices.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Henningsson, G.E., Isberg, A.M.: Velopharyngeal movement patterns in patients alternating between oral and glottal articulation: a clinical and cineradiographical study citation. Cleft Palate Journal 23(1), 1–9 (1986)
Murillo Rendón, S., Orozco Arroyave, J.R., Vargas Bonilla, J.F., Arias, J.D., Castellanos, C.G.: Automatic detections of hypernasality in children. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part II. LNCS, vol. 6687, pp. 167–174. Springer, Heidelberg (2011)
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 Int. Conf. on Spoken Language Processing, Brisbane, Australia, pp. 1757–1760 (2008)
Golding K.K.: Therapy Techniques for Cleft Palate Speech and Related Disorders. Singular Thomson Learning (2001)
Giovanni, A., Ouaknine, M., Guelfucci, R., Yu, T., Zanaret, M., Triglia, J.M.: Nonlinear behavior of vocal fold vibration: the role of coupling between the vocal folds. Journal of Voice 13(4), 456–476 (1999)
Henriquez, P., Alonso, J.B., Ferrer, M.A., Travieso, C.M., Godino-Llorente, J.I., Díaz-de-María, F.: Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics. IEEE Trans. on Audio, Speech and Language Processing 17(6), 1186–1195 (2009)
Delgado-Trejos, E., Sepúlveda, F.A., Röthlisberger, S., Castellanos-Domínguez, C.G.: The Rademacher Complexity Model over Acoustic Features for Improving Robustness in Hypernasal Speech Detection. In: Computers and Simulation in Modern Science, vol. V, pp. 130–135. WSEAS Press, University of Cambridge (2011)
Arias-Londoño, J.D., Godino-Llorente, J.I., 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. on Biomedical Engineering 58(2), 370–379 (2011)
Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Physica D 9, 189–208 (1983)
Rosenstein, M.T., Collins, J.J., De Luca, C.J.: A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65, 117–134 (1993)
Oseledec, V.A.: A multiplicative ergodic theorem. Lyapunov characteristic numbers for dynamical systems. Trans. of Moscow Mathematic Society 19, 197–231 (1968)
Kaspar, F., Shuster, H.G.: Easily calculable measure for complexity of spatiotemporal patterns. Physical Review A 36(2), 842–848 (1987)
Hurst, H.E., Black, R.P., Simaika, Y.M.: Long-term storage: an experimental study, London (1965)
Lee, G.S., Wang, C.P., Fu, S.: Evaluation of hypernasality in vowels using voice low tone to high tone ratio. Cleft Palate Journal 23(1), 47–52 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orozco-Arroyave, J.R., Murillo-Rendón, S., Vargas-Bonilla, J.F., Delgado-Trejos, E., Arias-Londoño, J.D., Castellanos-Domínguez, G. (2011). Nonlinear Dynamics for Hypernasality Detection. In: Travieso-González, C.M., Alonso-Hernández, J.B. (eds) Advances in Nonlinear Speech Processing. NOLISP 2011. Lecture Notes in Computer Science(), vol 7015. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25020-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-25020-0_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25019-4
Online ISBN: 978-3-642-25020-0
eBook Packages: Computer ScienceComputer Science (R0)