## 2 Corpora
### mPower  
Sponsored by Sage Bionetworks, the mPower corpus is unique because it uses crowdsourced audio samples--sustained vowel /\(\alpha\)://--from a mobile application. This corpus also includes other non-speech signals gathered such as finger-tapping dexterity and speed; short-term spatial memory; and walking gait/balance captured with the accelerometer of the cell phone. This is the largest corpus of its kind to date which is publicly available, with 5876 unique participants contributing a total of 65,022 recordings. Previous studies have used the mPower corpus not only to create and test PD detection models [12, 13, 14, 25, 27, 30, 36, 37], but also to design real-time PD diagnosis tools and applications [26], classify voice impairment level [38], measure longitudinal reliability and stability of these metrics [20], quantify and improve diagnosis techniques on signals recorded in noisy environments [39, 40], and even screen for symptoms of depression reported by PwPD [41]. Despite mPower's potential in the field of non-invasive PD detection, there are a few challenges to address when using this corpora. First, collecting data through a mobile application leads to high variability of recording conditions and microphones. Having an iPhone was a requirement for participation, but there were no restrictions on different models. Moreover, subjects were not monitored to ensure the quality in their recordings, so there are occasionally disingenuous or unsuccessful attempts at following the tasks. Another issue lies in the reliability of the participants' self-reported diagnosis, and whether or not they were examined by a medical professional. More subtly, the mPower corpora must be studied with caution, as it contains age and gender imbalances between classes. Additionally, other demographic aspects such as brain surgery or smoking must be considered since those can affect the quality of voice and could bias the results. In this study, we created a subset \(S\) (age demographics described in Table 4), selecting recordings based on the following inclusion criteria:  
* The speaker reported having a professional diagnosis;
* The speaker participated in the voice recording portion of the survey;
* The speaker reported never having brain surgery/deep brain stimulation;
* The speaker did not report a smoking history within the past ten years or for a total of over twenty years. We discarded participants that had brain surgery or deep brain stimulation as the influence of surgery therapies on the voice will need a separate analysis and might be different from typical PD phonatory patterns [42]. In the same manner, participants with a recent or long history of smoking were also discarded, as those participants could have voice characteristics different to the rest of participants that might also require a deeper study [43], which is out of the scope of this paper. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Female**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Total**} \\ \hline  & PD & **HC** & **PD** & **HC** & **PD** & **HC** \\ \hline Subjects & 9 & 12 & 19 & 10 & 28 & 22 \\ \hline Age, average (std) & 64.3 (12.2) & 65.3 (4.1) & 68.6 (6.4) & 69.3 (5.6) & 67.2 (8.7) & 67.1 (5.2) \\ \hline Age range & 40–80 & 60–72 & 50–77 & 60–77 & 40–80 & 60–77 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Age demographics from the ItalianPVS corpus, organized by gender and class. Then, we discarded all participants below the age of 30 to create our reference subset of usable participants \(S_{R}\) (Table 5). These participants were discarded as PwPD below 30 years old are atypical, might require a special analysis and, at the same time, most studies found on the literature do not consider them [20,25,30,37].