## 4 TL Approach  
TL allows the transfer of knowledge from a source domain to a target domain [55]. In most cases, a DNN is pre-trained on a large-scale database for a certain task, which we refer to as the source domain, and then re-trained for another task usually referred to as the target domain [55]. [PERSON] et al. [24] use three different architectures (SqueezeNet1_1, ResNet50, and DenseNet161) that have already been trained on ImageNet [56], a database consisting of more than 14 million images. ResNet and DenseNet are dense architectures while SqueezeNet is a lightweight architecture with fewer parameters [24]. At first, the authors freeze the convolutional layers of the three architectures, and only the fully connected layers are retrained to explore the learning rate from 1.0 \(\times\) 10\({}^{-6}\) to 1.0. Then, the convolutional layers are unfrozen and the learning rate dynamically updated. With a dynamically updated learning rate, the minimum loss can be determined more accurately. Of the three architectures, DenseNet161 obtained the highest accuracy (89.75%). Furthermore, the DenseNet-161 obtained a test time of 0.029 \(\pm\) 0.028 and a training time of 0.003 \(\pm\) 0.005, which is satisfactory and acceptable for clinical practice. [PERSON] et al. [5, 57] use the German Saarbruecken Voice Database [58] and the Colombian Spanish PC-GITA dataset as their main databases [36] in their cross-lingual studies. More information about both datasets can be found in Table 11. A cross-lingual architecture is pre-trained on one certain language, also called the base language, and is then applied to another language, which is often referred to as the target language. In [5], an ensemble of CNN networks is introduced using a Multiple Fine-Tuning (MFT) approach. The ensemble consists of three base CNN networks, of which each network is pre-trained on ImageNet but fine-tuned on a different dataset. The first CNN network is fine-tuned on PC-GITA whereas the second CNN network is fine-tuned on the Vowels dataset [59], and then further fine-tuned on PC-GITA. The third CNN network is fine-tuned on the Saarbruecken Voice Database and PC-GITA datasets. The Vowels dataset is not included in Table 11 since it does not contain any information about pathological diseases [5]. The CNN architectures used in this study are the ResNet50 and Xception networks. In general, fine-tuning these two types of CNNs on multiple databases (MFT) seems to boost the performance. For Xception, using MFT is associated with a higher accuracy in three of the five vowel utterance tasks. For ResNet50, this is the case for even four of these tasks. Furthermore, the ensemble of multiple fine-tuned CNNs generally performs better than the multiple fine-tuned CNNs individually. This shows the potential of both the MFT and ensemble approach proposed by these authors. In [57], [PERSON] et al. train and test a CNN network on a single dataset, unseen data and a mix of datasets. These datasets include the private Czech Parkinsonian dataset (CzechPD) [60], the PC-GITA dataset [36], the Italian [PERSON]'s voice and speech dataset [61], and the RMIT-PD dataset [62]. The authors of the experiment describe that the CNN performed well on each a single dataset, showing an accuracy of 90.82%, 90.52%, 97.81%, and 94.83% for the CzechPD, PC-GITA, Italian dataset, and RMIT-PD, respectively. However, when the CNN is tested on unseen data or a mix of the different datasets, the performance decreases significantly, leading to accuracy scores of between 43.07% and 78.85%. Compared to [PERSON] et al. [57], [PERSON] et al. [25] are able to obtain slightly better results with their TL approach. They obtained accuracy scores of between 61.5% and 77.3% with either Czech, German, or Spanish datasets as a base language and one of the remaining two as a target language. When the Spanish dataset is used as a base language, this generally led to higher performance (77.3% and 72.6%) than when Czech or German (70.0%, 72.0%, 76.7%, 70.7%) is used as a base language.