Skip to content

Advertisement

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact us so we can address the problem.

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

To access the full article, please see PDF.

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