Ruben Fernández, Jose Luis Blanco, Luis A. Hernández, Eduardo López, José Alcazar, Doroteo T. Toledano



The aim of this paper is to study new possibilities of using Automatic Speaker Recognition techniques (ASR) for detection of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases can be very useful to give priority to their early treatment optimizing the expensive and time-consuming tests of current diagnosis methods based on full overnight sleep in a hospital. This work is part of an on-going collaborative project between medical and signal processing communities to promote new research efforts on automatic OSA diagnosis through speech processing technologies applied on a carefully designed speech database of healthy subjects and apnoea patients. So far, in this contribution we present and discuss several approaches of applying generative Gaussian Mixture Models (GMMs), generally used in ASR systems, to model specific acoustic properties of continuous speech signals in different linguistic contexts reflecting discriminative physiological characteristics found in OSA patients. Finally, experimental results on the discriminative power of speaker recognition techniques adapted to severe apnoea detection are presented. These results obtain a correct classification rate of 81.25%, representing a promising result underlining the interest of this research framework and opening further perspectives for improvement using more specific speech recognition technologies.


  1. Blouet, R., Mokbel, C., Mokbel, H., Sanchez Soto, E., Chollet, G., & Greige, H. (2004). BECARS: a Free Software for Speaker Verification. In Proceedings of The Speaker and Language Recognition Workshop, ODYSSEY, pp 145-148.
  2. Coccagna, G., Pollini, A., & Provini, F. (2006). Cardiovascular disorders and obstructive sleep apnea syndrome. In Clinical and Experimental Hypertension Vol. 28:217-24.
  3. Do, M. N., (2003) Fast approximation of Kullback-Leibler distance for dependence trees and Hidden Markov Models. IEEE Signal Processing Letter 10, 115-118.
  4. Fernandez R., Hernández L. A., López E., Alcázar J., Portillo G., & Toledano D. T. (2008). Design of a Multimodal Database for Research on Automatic Detection of Severe Apnoea Cases. In Proceedings of 6th Language Resources and Evaluation Conference. LREC, Marrakech.
  5. Fiz, J.A., Morera, J., Abad, J., Belsulnces, A., Haro, M., Fiz, J.I., Jane, R., Caminal, P., & Rodenstein, D. (1993). Acoustic analysis of vowel emission in obstructive sleep apnea. In Chest Journal; 104: 1093 - 1096.
  6. Fox, A.W., & Monoson, P.K. (1993). Speech dysfunction of obstructive sleep apnea. A discriminant analysis of its descriptors. In Chest Journal; 96(3): 589-595.
  7. Fredouille, C., Pouchoulin, G., Bonastre, J.F., Azzarello, M., Giovanni, A., & Guio, A. (2005). Application of Automatic Speaker Recognition techniques to pathological voice assessment (dysphonia). In Proceeding of 9th European Conference on Speech Communication and Technology, Interspeech 2005, Lisboa, p. 149-152.
  8. Glass, J. , & Zue, V. (1985). Detection of nasalized vowels in American English. In Proceedings of Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP, Volume: 10, p. 1569- 1572.
  9. Hidalgo, A., & Quilis, M. (2002). Fonética y fonología españolas. Editorial Tirant blanch.
  10. Lloberes, P., Levy, G., Descals, C., et al. (2000). Selfreported sleepiness while driving as a risk factor for traffic accidents in patients with obstructive sleep apnoea syndrome and in non-apnoeic snorers. In Respiratory Medicine 94: 971-6.
  11. Moreno, A., Poch, D., Bonafonte, A., Lleida, E., Llisterri, J., Mariño, J.B., & Naude, C. (1993). ALBAYZIN Speech Database: Design of the Phonetic Corpus. In Proceedings of Eurospecch 93. Berlin, Germany, 21- 23. Vol. 1 pp. 175-178.
  12. Parsa, V., & Jamieson, D. G. (2001). Acoustic discrimination of pathological voice : Sustained vowels versus continuous speech. In Journal of Speech, Language, and Hearing Research ISSN 1092- 4388, vol. 44, no2, pp. 327-339 (1 p.1/4).
  13. Pruthi T. (2007) Analysis, vocal-tract modeling and automatic detection of vowel nasalization. Doctor Thesis at the University of Maryland.
  14. Puertas, F.J., Pin, G., María, J.M., & Durán, J. (2005). Documento de consenso Nacional sobre el síndrome de Apneas-hipopneas del sueño (SAHS). Grupo Español De Sueño (GES).
  15. Reynolds, D.A., Quatieri, T.F., & Dunn, R.B. (2000). Speaker verification using adapted gaussian mixture models. In Digital Signal Processing 10: 19-41.

Paper Citation

in Harvard Style

Fernández R., Blanco J., A. Hernández L., López E., Alcazar J. and T. Toledano D. (2009). SEVERE APNOEA DETECTION USING SPEAKER RECOGNITION TECHNIQUES . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 124-130. DOI: 10.5220/0001546601240130

in Bibtex Style

author={Ruben Fernández and Jose Luis Blanco and Luis A. Hernández and Eduardo López and José Alcazar and Doroteo T. Toledano},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
SN - 978-989-8111-65-4
AU - Fernández R.
AU - Blanco J.
AU - A. Hernández L.
AU - López E.
AU - Alcazar J.
AU - T. Toledano D.
PY - 2009
SP - 124
EP - 130
DO - 10.5220/0001546601240130