  Parkinson's disease patients develop different speech impairments that affect
their communication capabilities. The automatic assessment of the speech of the
patients allows the development of computer aided tools to support the
diagnosis and the evaluation of the disease severity. This paper introduces a
methodology to classify Parkinson's disease from speech in three different
languages: Spanish, German, and Czech. The proposed approach considers
convolutional neural networks trained with time frequency representations and a
transfer learning strategy among the three languages. The transfer learning
scheme aims to improve the accuracy of the models when the weights of the
neural network are initialized with utterances from a different language than
the used for the test set. The results suggest that the proposed strategy
improves the accuracy of the models in up to 8\% when the base model used to
initialize the weights of the classifier is robust enough. In addition, the
results obtained after the transfer learning are in most cases more balanced in
terms of specificity-sensitivity than those trained without the transfer
learning strategy.

Machine Learning;Computation and Language;Audio and Speech Processing;Machine Learning