GMM-based Classifiers for the Automatic Detection of Obstructive Sleep Apnea

The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those systern^ include the usage of a^optimized representation of the spectral envelope, either based on cepstral coefficients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefficients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefficients) for characterization, and Gaussian Mixture Models for posterior classification. However, the study of recently proposed GMM-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered in pathology detection labours. The present work aims at testing whether or not the employmenl|of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specifically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs^arj^Ob3tructivo Sloop Apnoa database^ in conjunction with GMM-based classifiers looking for a better performance. The results show that an improved performance might be obtained by using such approach.