Effects of audio compression in automatic detection of voice pathologies.
This paper investigates the performance of an automatic system for voice pathology detection when the voice samples have been compressed in MP3 format and different binary rates (160, 96, 64, 48, 24, and 8 kb/s). The detectors employ cepstral and noise measurements, along with their derivatives, to characterize the voice signals. The classification is performed using Gaussian mixtures models and support vector machines. The results between the different proposed detectors are compared by means of detector error tradeoff (DET) and receiver operating characteristic (ROC) curves, concluding that there are no significant differences in the performance of the detector when the binary rates of the compressed data are above 64 kb/s. This has useful applications in telemedicine, reducing the storage space of voice recordings or transmitting them over narrow-band communications channels.
Sáenz-Lechón
Nicolás
N
Department of Ingeniería de Circuitos y Sistemas, Universidad Politécnica de Madrid, 28031 Madrid, Spain. nicolas.saenz@upm.es
Osma-Ruiz
Víctor
V
Godino-Llorente
Juan I
JI
Blanco-Velasco
Manuel
M
Cruz-Roldán
Fernando
F
Arias-Londoño
Julián D
JD
D016477:Artifacts; D001185:Artificial Intelligence; D044962:Data Compression / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D019212:Multimedia; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D012372:ROC Curve; D013018:Sound Spectrography / Q000379:methods*; D013061:Speech Acoustics; D014831:Voice; D014832:Voice Disorders / Q000175:diagnosis* / Q000503:physiopathology; D014833:Voice Quality