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EURASIP Journal on Advances in Signal Processing
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Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

EURASIP Journal on Advances in Signal Processing volume 2009, Article number: 982531 (2009) Cite this article

Abstract

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

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Authors and Affiliations

  1. Signal, Systems and Radiocommunications Department, Universidad Politécnica de Madrid, Madrid, 28040, Spain

    Rubén Fernández Pozo, Jose Luis Blanco Murillo, Luis Hernández Gómez & Eduardo López Gonzalo

  2. Respiratory Department, Hospital Torrecárdenas, Almería, 04009, Spain

    José Alcázar Ramírez

  3. ATVS Biometric Recognition Group, Universidad Autónoma de Madrid, Madrid, 28049, Spain

    Doroteo T. Toledano

Authors
  1. Rubén Fernández Pozo
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  2. Jose Luis Blanco Murillo
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  3. Luis Hernández Gómez
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  4. Eduardo López Gonzalo
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  5. José Alcázar Ramírez
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  6. Doroteo T. Toledano
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Corresponding author

Correspondence to Rubén Fernández Pozo.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fernández Pozo, R., Blanco Murillo, J.L., Hernández Gómez, L. et al. Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques. EURASIP J. Adv. Signal Process. 2009, 982531 (2009). https://doi.org/10.1155/2009/982531

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  • DOI: https://doi.org/10.1155/2009/982531

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