  This work is concerned with devising a robust Parkinson's (PD) disease
detector from speech in real-world operating conditions using (i) foundational
models, and (ii) speech enhancement (SE) methods. To this end, we first
fine-tune several foundational-based models on the standard PC-GITA (s-PC-GITA)
clean data. Our results demonstrate superior performance to previously proposed
models. Second, we assess the generalization capability of the PD models on the
extended PC-GITA (e-PC-GITA) recordings, collected in real-world operative
conditions, and observe a severe drop in performance moving from ideal to
real-world conditions. Third, we align training and testing conditions
applaying off-the-shelf SE techniques on e-PC-GITA, and a significant boost in
performance is observed only for the foundational-based models. Finally,
combining the two best foundational-based models trained on s-PC-GITA, namely
WavLM Base and Hubert Base, yielded top performance on the enhanced e-PC-GITA.
