Transfer Learning to Detect Parkinson's Disease from Speech In Different Languages Using Convolutional Neural Networks with Layer Freezing

Parkinson's Disease is a neurodegenerative disorder characterized by motor symptoms such as resting tremor, bradykinesia, rigidity and freezing of gait. The most common symptom in speech is called hypokinetic dysarthria, where speech is characterized by monotone intensity, low pitch variability and poor prosody that tends to fade at the end of the utterance. This study proposes the classification of patients with Parkinson's Disease and healthy controls in three different languages (Spanish, German, and Czech) using a transfer learning strategy. The process is further improved by freezing consecutive different layers of the architecture. We hypothesize that some convolutional layers characterize the disease and others the language. Therefore, when a fine-tuning in the transfer learning is performed, it is possible to find the topology that best adapts to the target language and allows an accurate detection of Parkinson's Disease. The proposed methodology uses Convolutional Neural Networks trained with Mel-scale spectrograms. Results indicate that the fine-tuning of the neural network does not provide good performance in all languages while fine-tuning of individual layers improves the accuracy by up to 7%. In addition, the results show that Transfer Learning among languages improves the performance in up to 18% when compared to a base model used to initialize the weights of the network.