Severe apnoea detection using speaker recognition techniques
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Institute for Systems and Technologies of Information, Control and CommunicationDate
2009-01Citation
10.5220/0001546601240130
International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2009). INSTICC, Porto (Portugal), 14-17 January 2009
DOI
10.5220/0001546601240130Funded by
The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-01 project.Editor's Version
http://dx.doi.org/10.5220/0001546601240130Subjects
Apnoea; Automatic Speaker Recognition techniques; GMM; nasalization; Medicina; TelecomunicacionesNote
Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2009)Rights
Copyright 2009 - INSTICC and/or its suppliers. All rights reservedAbstract
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 timeconsuming
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
Files in this item
Google Scholar:Fernández Pozo, Rubén
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Blanco, José Luis
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Hernández, Luis Alberto
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López Gonzalo, Eduardo
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Alcázar Ramírez, José
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Toledano, Doroteo T.
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