  Recently proposed automatic pathological speech classification techniques use
unsupervised auto-encoders to obtain a high-level abstract representation of
speech. Since these representations are learned based on reconstructing the
input, there is no guarantee that they are robust to pathology-unrelated cues
such as speaker identity information. Further, these representations are not
necessarily discriminative for pathology detection. In this paper, we exploit
supervised auto-encoders to extract robust and discriminative speech
representations for Parkinson's disease classification. To reduce the influence
of speaker variabilities unrelated to pathology, we propose to obtain speaker
identity-invariant representations by adversarial training of an auto-encoder
and a speaker identification task. To obtain a discriminative representation,
we propose to jointly train an auto-encoder and a pathological speech
classifier. Experimental results on a Spanish database show that the proposed
supervised representation learning methods yield more robust and discriminative
representations for automatically classifying Parkinson's disease speech,
outperforming the baseline unsupervised representation learning system.
