However, to our knowledge, there are no studies analyzing the influence of these segments on accuracy. Moreover, many studies employ large feature vectors obtained with feature extraction libraries to characterize the phonation recordings without reasoning or selecting the most appropriate features for PD detection. In this respect, a recent letter [21] to the editors of a publication reveals that the use of high-dimensional feature vectors with small data sets (100 times smaller than the feature vector dimension, in that case) leads to overoptimisticclassification accuracy and non-reproducible results. The reasoning behind this idea is that the larger the feature vector, the more chances to find one type of feature that might randomly correlate with the classes to be classified without any scientific grounding. The validation strategies found in studies employing phonatory aspects to detect PD are also diverse. The most common is cross-validation, in which the employed corpus is divided into multiple folds that are used to sequentially train and test several models, yielding a weighted average accuracy from the individual models. It is uncommon to find studies which employ different corpora in model training and testing (cross-corpora validation) [22]. In some cases, the authors do not use cross-validation, and divide the corpus into training and testing subsets randomly [14, 22, 23, 24, 25, 26]. This increases the uncertainty of the results when using small corpora (usually no more than 3 h of recordings), as the testing partition is not large enough to be considered representative. Moreover, some studies where there are more than one recording per participant ensure that recordings from a speaker used to train a model are not considered to test or validate the same model (subject-wise split) [19, 25, 27], whereas other studies randomly select the recordings employed in the training and testing processes or intentionally use recordings from the same speakers in training and testing subsets (record-wise split) [11, 14]. The latter studies do not consider that when both training and testing processes contain speaker identity information encoded in sustained vowels, their classifiers may be detecting characteristics of specific speakers in addition to PD patterns. This could produce overoptimistic results, as coefficients like MFCCs and Perceptual Linear Prediction (PLP), employed as the input of the classifier, have notably succeeded in speaker recognition tasks [28, 29] and could lead to classifier bias. Motivated by this discrepancy, several studies [13, 16, 30] have reported a marked difference in accuracy between randomly populating folds in cross-validation with (1) single recordings at a time (record-wise); and (2) all recordings from a single participant (subject-wise), with record-wise experiments leading to artificially higher accuracy. This observation also arises in other speech-based medical evaluation models, such as one which estimates depression level from audio recordings [31]. Lastly, each study tends to employ their own corpus, due to the scarcity of publicly available data sets to compare results across different methodologies. As a result of the differences in data collection, populations, and methodologies, the literature provides a variety of results that sometimes lead to contradictory or controversial conclusions regarding which acoustic features better characterize the presence of PD. This methodological variability and the inclusion of methodological issues makes it hard to distinguish between valuable and minor/misleading contributions. For instance, it is not possible to know if the high detection accuracy reported in some studies is caused by the acoustic patterns of PD in the voice of the participants or because the average age difference between PwPD and controls was higher than ten years.