Subject independent identification of breath sounds components using multiple classifiers
Breath sounds have been shown very valuable for diagnosis of obstructive sleep apnea. In this study, we present a subject independent method for automatic classification of breath and related sounds during sleep. An experienced operator manually labelled segments of breath sounds from 11 sleeping subjects as: inspiration, expiration, inspiratory snoring, expiratory snoring, wheezing, other noise, and non-audible. Ten features were extracted and fed into 3 different classi
This paper presents BUT system submitted to NIST 2008 SRE. It includes two subsystems based on Joint Factor Analysis (JFA) GMM/UBM and one based on SVM-GMM. The systems were developed on NIST SRE2006 data, and the results arepresented on NIST SRE 2008 evaluation data. We concentrate on the influence of side information in the calibration. Index Terms: speaker recognition, joint factor analysis, NIST SRE 2008.