Automatic detection of obstructive sleep apnea using speech signal analysis

Oren Elisha, Ariel Tarasiuk, Yaniv Zigel

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish GB
StatePublished - 2012


  • obstructive sleep apnea
  • speech signal processing
  • Speaker recognition
  • phoneme identification


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