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  • Research Article
  • Open Access

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

  • 1Email author,
  • 1,
  • 1,
  • 1,
  • 2 and
  • 3
EURASIP Journal on Advances in Signal Processing20092009:982531

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

  • Received: 1 November 2008
  • Accepted: 8 May 2009
  • Published:

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.

Keywords

  • Gaussian Mixture Model
  • Obstructive Sleep Apnoea
  • Automatic Speech Recognition
  • Speaker Recognition
  • Correct Classification Rate

Publisher note

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Authors’ Affiliations

(1)
Signal, Systems and Radiocommunications Department, Universidad Politécnica de Madrid, Madrid, 28040, Spain
(2)
Respiratory Department, Hospital Torrecárdenas, Almería, 04009, Spain
(3)
ATVS Biometric Recognition Group, Universidad Autónoma de Madrid, Madrid, 28049, Spain

Copyright

© Rubén Fernández Pozo et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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