  Automatic objective non-invasive detection of pathological voice based on
computerized analysis of acoustic signals can play an important role in early
diagnosis, progression tracking and even effective treatment of pathological
voices. In search towards such a robust voice pathology detection system we
investigated 3 distinct classifiers within supervised learning and anomaly
detection paradigms. We conducted a set of experiments using a variety of input
data such as raw waveforms, spectrograms, mel-frequency cepstral coefficients
(MFCC) and conventional acoustic (dysphonic) features (AF). In comparison with
previously published works, this article is the first to utilize combination of
4 different databases comprising normophonic and pathological recordings of
sustained phonation of the vowel /a/ unrestricted to a subset of vocal
pathologies. Furthermore, to our best knowledge, this article is the first to
explore gradient boosted trees and deep learning for this application. The
following best classification performances measured by F1 score on dedicated
test set were achieved: XGBoost (0.733) using AF and MFCC, DenseNet (0.621)
using MFCC, and Isolation Forest (0.610) using AF. Even though these results
are of exploratory character, conducted experiments do show promising potential
of gradient boosting and deep learning methods to robustly detect voice
pathologies.
