Using modulation spectra for voice pathology detection and classification.
In this paper, we consider the use of Modulation Spectra for voice pathology detection and classification. To reduce the high-dimensionality space generated by Modulation spectra we suggest the use of Higher Order Singular Value Decomposition (SVD) and we propose a feature selection algorithm based on the Mutual Information between subjective voice quality and computed features. Using SVM with a radial basis function (RBF) kernel as classifier, we conducted experiments on a database of sustained vowel recordings from healthy and pathological voices. For voice pathology detection, the suggested approach achieved a detection rate of 94.1% and an Area Under the Curve (AUC) score of 97.8%. For voice pathology classification, an average detection rate and AUC of 88.6% and 94.8%, respectively, was achieved in classifying polyp against keratosis leukoplakia, adductor spasmodic dysphonia and vocal nodules.
Markaki
Maria
M
Department of Computer Science, University of Crete, 71409 Crete, Greece. mmarkaki@csd.uoc.gr
Stylianou
Yannis
Y
D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000295:instrumentation* / Q000379:methods; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D014832:Voice Disorders / Q000175:diagnosis / Q000503:physiopathology*; D014833:Voice Quality