## 2 Materials and Methods
### Feature Extraction  
Frame-by-frame fundamental frequency (F0), creak, and cepstral peak prominence (CPP) contours were computed for each single-task audio file. Praat was used to generate a voice report to determine additional measures of vocal stability, and speech phrase durations were used as measures of respiratory capacity [35]. Finally, MPT was calculated as the duration between the first and last voiced frames for each MPT task recording. Most features were extracted as described in [28], with the following exceptions.  
Measures of speech phrase duration were computed for the continuous speech tasks. Within an utterance, pause durations longer than 500 ms were assumed to contain an inhalation [18]. A speech phrase was defined as the speech between two successive inhalations. In other words, voiced regions separated from each other by less than 500 ms were considered to be part of the same speech phrase. Measures of breath capacity that were computed following this method included the number of speech phrases, total duration of all the speech phrases, and mean, median, and standard deviation (SD) of the speech phrase durations in each utterance.  
F0 was extracted with Praat's cross-correlation pitch-tracking method, which uses a 40 ms Hanning window every 3.3 ms. To minimize distortion from tracking artifacts, only frames with F0 in the 5-95 th percentile range for a task were used to compute mean F0, median F0, and F0 SD. For sustained vowel tasks, the F0 SD computation was further restricted to frames with F0 within 50 Hz of the median F0. Using this 100 Hz range allowed the F0 SD calculation to include normal pitch fluctuations while reducing the effect of tracking errors that undesirably doubled or halved the true F0. Other acoustic perturbation features derived from the [PERSON] voice report (jitter, shimmer, harmonics-to-noise ratio, and the low-high spectral energy ratio) were computed from the center 500 ms of each vowel to avoid instability from voicing onsets and offsets.  
For each task, the probability of creak, an acoustic feature related to irregularity and vocal fry perception, was computed every 10 ms using a method developed by [36] and [37]. Acoustic features relating to short-term power contours, intra-frame periodicity, inter-pulse similarity, and the presence of secondary or widely spaced glottal pulses were used as inputs to an artificial neural network that outputs frame-by-frame creak probabilities. Here, frames were considered voiced if they (1) had non-zero F0 based on the [PERSON] pitch track, (2) occurred within a speech phrase, or (3) had a creak probability above 0.8. Adding high-probability creak frames additionally accounted for frames with highly irregular phonation--often on the margins of voiced regions--where [PERSON]'s pitch tracking did not identify an underlying F0. For each utterance, the creak percent was given by (# creaky frames)/(# voiced frames) \(\times\) 100. The \"# creaky frames\" quantity was defined as voiced frames with creak probability either above 0.02 ([PERSON] et al., 2016), or, separately, above 0.3 ([PERSON] et al., 2016).  
Cepstral peak prominence (CPP) was used as a measure of degraded voice quality or dysphonia, with higher CPP indicating healthier voice quality ([PERSON] et al., 2016). CPP and CPP SD were calculated for both vowel and Rainbow Passage utterances following ([PERSON] et al., 2016). The CPP was required to fall between quefrencies of 3.3 and 16.7 ms, which are equivalent to F0 between 60 and 300 Hz. In that quefrency range, CPP was given by the difference between the magnitude of the highest peak and the power cepstrum's noise floor (window length 40.96 ms, computed every 10.24 ms). CPP was reported only for frames that met one of the three voicing criteria used for the F0 measures: frames with positive F0, within a speech phrase, or with creak probability above 0.8. For each task, frames with CPP between the 5 th and 95 th percentile were used to compute mean, median, and SD of CPP.