## 5 DAFE Approach  
DAFE refers to deep features that are automatically extracted from an audio signal using a DL model. These features are either learned in the final layers of the DL model or used as input for a ML model [11]. There are a few recent studies that employ DAFE for PD classification using different DL and ML approaches. For instance, [PERSON] et al. [66] propose a stacked auto-encoder DNN framework to classify PD and HC using speech material from the PC-GITA database. They achieve an accuracy of 87% using time-frequency deep features from spectrograms and a softmax deep classifier in the final layer, outperforming a classical ML-based SVM classifier (which achieves an accuracy of 83%). [PERSON] et al. [12] aim to understand how well a classification model trained on DAFE in one language works in a different target language. To investigate this, they use three different architectures (Wav2 Vec2.0, VGGish, and SoundNet) to generate the DAFE. They alternate between English and Telugu as source and target languages, with Telugu being an under-resourced language. This could affect the accuracy of PD classification in this language. However, the results in [12] show that an accuracy higher than 90% is achieved for both English and Telugu, despite Telugu being an under-resourced language. This accuracy is observed for both the traditional features (MFCCs, F0, jitter, shimmer) and the DAFE. Another study that uses DAFE in multi-lingual and cross-lingual settings is carried out by [PERSON] et al. [13]. They compare the performance of interpretable feature-based models (IFM) and non-interpretable feature-based models (NIFM). They also consider pitch, loudness, and variation as interpretable features, similar to [PERSON] et al. [12]. Non-interpretable features are the DNN embeddings extracted with Wav2 Vec2.0, TRILLsson, and HuBERT. According to the authors, their work is the first to apply the TRILLsson and HuBERT representations to PD diagnosis and Wav2 Vec2.0 in multi-lingual and cross-lingual settings [13]. They show that NIFMs outperform IFMs in mono-lingual, multi-lingual, and cross-lingual settings. In the mono-lingual setting, IFMs achieve 4% lower accuracy than NIFMs. In the multi-lingual setting, where classifiers are trained on each language separately, NIFMs outperform IFMs by 7%. This improvement over the mono-lingual setting suggests that using more languages enhances PD diagnosis. In the cross-lingual setting, where classifiers are trained on all languages except the target language, NIFMsagain outperform IFMs, this time by 5.8% in terms of accuracy. One of the datasets [PERSON] et al. [13] use is the NeuroVoz database [67], detailed in Table A1. [PERSON] et al. [9] also use DAFE, which they call X-vectors, instead of traditional MFCCs. They demonstrate that PD detection performs better in men due to greater variability in female MFCC distributions, which complicates MFCC-based classification in women, despite the improvements obtained with X-vectors of from 7% to 15% in terms of accuracy. [PERSON] et al. [68] introduce deep dual-side learning, comprising both deep feature learning and deep sample learning. For deep feature learning, the authors use an embedded stack group sparse auto-encoder (EGSAE) to acquire the deep features. Then, these deep features are fused with the original features using L1 regularization. This results in a hybrid feature set. For deep sample learning, hierarchical sample spaces are created using an iterative clustering algorithm (IMC). Classification models are applied to each of these sample spaces. A weighted fusion mechanism fuses the different models into an ensemble model, thereby combining deep feature and deep sample learning. With this method, accuracy scores of 98.4% and 99.6% are obtained on the LSVT Voice Rehabilitation Dataset [69] and the Parkinson's Disease Classification dataset [45], respectively. Whereas the Parkinson's Disease Classification dataset is publicly available (see Table A1), the raw audio files of the LSVT dataset are private.