Design of a multimodal database for research on automatic detection of severe apnoea cases

Fernández Pozo, Rubén and Hernández Gómez, Luis Alfonso and Lopez Gonzalo, Eduardo and Alcazar, Jose and Portillo, Guillermo and Torre Toledano, Doroteo (2008). Design of a multimodal database for research on automatic detection of severe apnoea cases. In: "6th International conference on Language Resources and Evaluation, LREC 2008", 26/05/2008-01/06/2008, Marrakech, Marruecos. ISBN 2-9517408-4-0.

Description

Title: Design of a multimodal database for research on automatic detection of severe apnoea cases
Author/s:
  • Fernández Pozo, Rubén
  • Hernández Gómez, Luis Alfonso
  • Lopez Gonzalo, Eduardo
  • Alcazar, Jose
  • Portillo, Guillermo
  • Torre Toledano, Doroteo
Item Type: Presentation at Congress or Conference (Article)
Event Title: 6th International conference on Language Resources and Evaluation, LREC 2008
Event Dates: 26/05/2008-01/06/2008
Event Location: Marrakech, Marruecos
Title of Book: CD-ROM Proceedings of the 6th International conference on Language Resources and Evaluation, LREC 2008
Date: 2008
ISBN: 2-9517408-4-0
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The aim of this paper is to present the design of a multimodal database suitable for research on new possibilities for automatic diagnosis 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 groups towards the design of a multimodal database as an innovative resource to promote new research efforts on automatic OSA diagnosis through speech and image processing technologies. In this contribution we present the multimodal design criteria derived from the analysis of specific voice properties related to OSA physiological effects as well as from the morphological facial characteristics in apnoea patients. Details on the database structure and data collection methodology are also given as it is intended to be an open resource to promote further research in this field. Finally, preliminary experimental results on automatic OSA voice assessment are presented for the collected speech data in our OSA multimodal database. Standard GMM speaker recognition techniques obtain an overall correct classification rate of 82%. This represents an initial promising result underlining the interest of this research framework and opening further perspectives for improvement using more specific speech and image recognition technologies.

More information

Item ID: 4309
DC Identifier: http://oa.upm.es/4309/
OAI Identifier: oai:oa.upm.es:4309
Official URL: http://www.lrec-conf.org/proceedings/lrec2008/summaries/454.html
Deposited by: Memoria Investigacion
Deposited on: 27 Sep 2010 09:21
Last Modified: 20 Apr 2016 13:35
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