  The detection of pathologies from speech features is usually defined as a
binary classification task with one class representing a specific pathology and
the other class representing healthy speech. In this work, we train neural
networks, large margin classifiers, and tree boosting machines to distinguish
between four pathologies: Parkinson's disease, laryngeal cancer, cleft lip and
palate, and oral squamous cell carcinoma. We show that latent representations
extracted at different layers of a pre-trained wav2vec 2.0 system can be
effectively used to classify these types of pathological voices. We evaluate
the robustness of our classifiers by adding room impulse responses to the test
data and by applying them to unseen speech corpora. Our approach achieves
unweighted average F1-Scores between 74.1% and 97.0%, depending on the model
and the noise conditions used. The systems generalize and perform well on
unseen data of healthy speakers sampled from a variety of different sources.
