Memorias de investigación
Research Publications in journals:
Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech
Year:2012
Research Areas
  • Processing and signal analysis
Information
Abstract
We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech.
International
Si
JCR
Si
Title
Cognitive Computation
ISBN
1866-9956
Impact factor JCR
1
Impact info
Volume
4
10.1007/s12559-012-9168-x
Journal number
2
From page
1
To page
15
Month
AGOSTO
Ranking
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Aplicaciones del Procesado de Señal (GAPS)
  • Departamento: Señales, Sistemas y Radiocomunicaciones