As a voice signal, the advantages of sustained phonations is that they provide information about tremor and rigidity in the respiratory and glottal structures and the subjects are not required to read or memorize a sentence. Furthermore, as the detection models based on phonatory features do not rely on articulatory-specific information, these can be sometimes considered multilingual or are easily adapted to be employed in populations with different mother tongues. However, sustained phonation signals are less complex than those from connected speech, which require the precise coordination of multiple articulators while also conveying phonatory information. This makes connected speech more suitable in the analysis of kinetic aspects. Although the interest in the development of diagnostic tools employing Persons with Parkinson's Disease (PwPD)' phonation is increasing [4], there is not a clear consensus about the validity of the methodologies proposed in the literature, as different data sets and validation procedures vary between studies. Certain experiments have suggested that machine learning and signal processing can automatically distinguish between PwPD and controls with significant confidence. However, some of these papers contain methodological issues that are known and understood in the machine learning community. For example, employing separate corpora for each class [9,10] may train classifiers to detect PD via meta-characteristics such as environmental noise, the sampling frequency, or the recording technology of each corpus. Another common problem identified in the literature is that some studies employ data sets in which the average age difference of speakers with and without PD exceeds 5 years [10, 11, 12, 13, 14, 15]. Generally, these studies report high recall in their trials of automatic PD detection. They have not considered that since age influences speech; their classifiers may be detecting characteristics related to age difference between groups in lieu of PD patterns. This could lead to overoptimistic results as the influence of age in speech has been shown to help differentiate between classes [16]. However, details of the examined class demographics and speaker statistics (e.g., age, sex, and stage of the disease, at the very least), are not always regularly reported, potentially impacting conclusions. Some articles [17] claim that using sustained vowels leads to better results than other speaking tasks in detecting differences between speakers with and without PD. Using sustained vowels, word utterances, and short sentence readings, this study measures amplitude/frequency perturbation qualities, such as jitter, shimmer, Harmonic to Noise Ratio (HNR), Noise to Harmonics Ratio (NHR) and other pitch statistics. However, one may consider it unorthodox to use these coefficients on non-sustained vowels because quantifying the quality of the phonation is their principal use [18]. Connected speech includes prosody and articulation, which both cause major modulations in amplitude and frequency. As such, using amplitude and frequency perturbation measures such as jitter and shimmer on voiceless consonants, for instance, might lead to incorrect conclusions. There is no inherent meaning of these features when measured in voiceless consonants. When considering sustained vowels, some corpora are distributed with the onset and offset of the phonation, while others have trimmed each recording to contain only the middle, most stable, segment. This is usually more adequate when employing acoustic analysis or features proposed to be used in the stable part of the phonation, such as complexity or frequency and amplitude perturbation features [18]. Therefore, some studies take corpora distributed with the full recording and shorten them to as short as one second [14,19,20]. Nevertheless, the onset and offset contain articulatory information and might add extra differentiation between classes, especially when employing certain features such as Mel-Frequency Cepstral Coefficients (MFCC) to characterize them.