Analysis of speech signals among obstructive sleep apnea patients

Abstract

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder. Previous studies have confirmed that OSA is associated with anatomical abnormalities of the upper airways. Acoustic parameters of human speech are significantly influenced by the vocal tract structure and soft tissue properties; therefore, there is reason to believe that there is correlation between speech signal parameters and the existence of OSA. This work aims to explore the influence of OSA on acoustic speech features. Signal processing and pattern recognition algorithms were developed to differentiate between OSA and non-OSA subjects using their speech signals. Using Gaussian mixture model (GMM) classifier and a speech database of 13 non-OSA and 13 OSA diagnosed adult male subjects, an equal error rate (EER) of 7.7% was achieved. These results show that acoustic features from speech signals of awake subjects can predict OSA, and can be used as a tool for initial screening of potential patients.