| 1D-CNN | One-Dimensional Convolutional Neural Network |
| 2D-CNN | Two-Dimensional Convolutional Neural Network |
| C | PD-Czech |
| CNN | Convolutional Neural Network |
| DA | Domain Adversarial |
| DL | Deep Learning |
| DDK | Diadochokinetic test |
| G | PD-GITA |
| Ger | PD-German |
| GRL | Gradient Reversal Layer |
| HC | Healthy control |
| H&Y | Hoehn and Yahr Scale |
| KL | Kullback–Leibler |
| LSTM | Long Short-Term Memory |
| MC | Mixed Corpora |
| MLP | Multilayer Perceptron |
| N | PD-Neurovoz |
| PD | Parkinson’s Disease |
| RNN | Recurrent Neural Network |
| SC | Single Corpus |
| SGD | Stochastic Gradient Descent |
| SVDD | Saarbrücken Voice Disorders Database |
| TCM | Trace of the Covariance Matrix |
| Time-CNN-LSTM | Time-Distributed CNN and LSTM Networks |
| TL | Transfer Learning |
| t-SNE | Stochastic Neighbour Embeddings Distributed by t |
| UPDRS | Unified Parkinson’s Disease Rating Scale |