ISCA Archive AVIOS 2012
ISCA Archive AVIOS 2012

Automatic detection of obstructive sleep apnea using speech signal analysis

Oren Elisha, Ariel Tarasiuk, Yaniv Zigel

Obstructive sleep apnea (OSA) is a sleep disorder associated with several anatomical abnormalities of the upper airway. Our hypothesis is that it is possible to distinguish between OSA and non-OSA subjects by analyzing particular speech signal properties using an automatic computerized system. The database for this research was constructed from 90 male subjects who were recorded reading a one-minute speech protocol immediately prior to a full polysomnography study; specific phonemes were isolated using closed group phoneme identification; seven independent Gaussian mixture models (GMM)-based classifiers were implemented for the task of OSA / non-OSA classification; a fusion process was designed to combine the scores of these classifiers and a validation procedure took place in order to examine the system’s performance. Results of 91.66% specificity and 91.66% sensitivity were achieved using a leave one out procedure when the data was manually segmented. The system performances were somewhat decreased when the automatic segmentation was used, resulting in 83.33% specificity and 81.25% sensitivity.

Index Terms. Obstructive sleep apnea, speech signal processing, speaker recognition, phoneme identification.

Cite as: Elisha, O., Tarasiuk, A., Zigel, Y. (2012) Automatic detection of obstructive sleep apnea using speech signal analysis. Proc. Afeka-AVIOS Speech Processing Conference, 20-23

  author={Oren Elisha and Ariel Tarasiuk and Yaniv Zigel},
  title={{Automatic detection of obstructive sleep apnea using speech signal analysis}},
  booktitle={Proc. Afeka-AVIOS Speech Processing Conference},