As one of the most prevalent neurodegenerative disorders, Parkinson’s disease (PD)
has a significant impact on the fine motor skills of patients. The complex interplay of
different articulators during speech production and realization of required muscle
tension become increasingly difficult, thus leading to a dysarthric speech.
Characteristic patterns such as vowel instability, slurred pronunciation and slow
speech can often be observed in the affected individuals and were analyzed in
previous studies to determine the presence and progression of PD. In this work, we
used a phonetic recognizer trained exclusively on healthy speech data to investigate
how PD affected the phonetic footprint of patients. We rediscovered numerous patterns
that had been described in previous contributions although our system had never seen
any pathological speech. Furthermore, we could show that intermediate activations
from the neural network could serve as feature vectors encoding information related to
the disease state of individuals. We were also able to directly correlate the expert-rated
intelligibility of a speaker with the mean confidence of phonetic predictions. Our results
support the assumption that pathological data is not necessarily required to train
systems that are capable of analyzing PD speech.