## 3 Results
### Performance of Manual and Semi-Automated RFF Estimation Methods  
The comparison of aRFF-APH, aRFF-AP, and manual RFF estimation techniques in identifying the true boundary cycle is shown in Figure 5. Out of 7721 offset instances (see Figure 5a), the aRFF-APH algorithms correctly identified the boundary cycle in 72.1% of instances (N = 5565). The aRFF-AP algorithm correctly identified the boundary cycle in 65.0% of offset instances (N = 5021), followed by manual RFF estimation in which only 12.9% of offset boundary cycles (N = 994) were correctly identified. The proportion of correct boundary cycle identifications by cohort is shown in Table 5. When using manual RFF estimation, boundary cycles were correctly classified for participants with typical voices less than a third of the time (14.7% for voicing offset, 30.4% for voicing onset). The proportion of correctly identified boundary cycles decreased for the majority of individuals with HVDs, including those with Parkinson's disease (10.9% for voicing offset, 27.2% for voicing onset) and muscle tension dysphonia (8.3% for voicing offset, 20.0% for voicing onset). When using the aRFF-AP algorithm, offset boundary cycles were correctly classified  
Figure 4: Normalized feature values calculated from the raw microphone signal or Auditory-SWIPE’ output (teal) with respect to distance (pitch periods) from the true boundary cycle (thin black dotted line) for voicing onset. Normalized feature values calculated from band-pass filtered microphone signal are overlaid in orange (when applicable). Top row: normalized peak-to-peak amplitude (PTP), short-time magnitude (STM), short-time energy (STE), cross-correlation (XCO), normalized cross-correlation (NXCO), autocorrelation (ACO). Middle row: mean and standard deviation of cepstral peak prominence (CPP, SD CPP), signal-to-noise ratio (SNR), number of zero crossings (NZC), waveform shape similarity (WSS), low-to-high ratio of spectral energy (LHR). Bottom row: log energy (LE), average and median pitch strength (APS, MPS), average, median, and standard deviation of \(f_{o}\) (\(\text{A}f_{o}\), \(\text{M}f_{o}\), \(\text{SD}f_{o}\)). Thick solid lines indicate mean values of features that were retained after manual inspection. Thick orange and teal dashed lines indicate mean values of features that were removed through manual inspection. Shaded regions indicate standard deviation. for a larger percentage of typical voices (66.9%) than HVDs, including Parkinson's disease (58.9%) and muscle tension dysphonia (66.1%). Using the aRFF-APH algorithm did not produce this trend, instead showing similar proportions between cohorts. Interestingly, the proportion of correctly identified onset boundary cycles for participants with typical voices relatively low for aRFF-AP (72.4%) and aRFF-APH algorithms (76.8%); this is in contrast to, for instance, individuals with Parkinson's disease (78.1% for aRFF-AP, 81.9% for aRFF-APH) and muscle tension dysphonia (83.8% for aRFF-AP, 88.5% for aRFF-APH). All offset and onset proportions were, however, greater across all cohorts when using the aRFF-AP or aRFF-APH algorithms instead of manual estimation. Misclassifications occurred at the rate of 25.6% for aRFF-APH (N = 1978), 32.2% for aRFF-AP (N = 2488), and 74.2% for manual RFF estimation (N = 5730). Nearly 13% of offset instances (N = 997) were rejected during manual estimation, whereas under 3% of offset instances were rejected by the aRFF-APH (N = 178) and aRFF-AP (N = 212) algorithms. For the algorithmic methods, three offset instances were automatically rejected due to pitch strength values < 0.05. The remainder of these rejections were due to errors in identifying voiced cycles (N = 151 for aRFF-AP, N = 150 for aRFF-APH), or post-processing of resulting RFF values (e.g., glottalization; N = 58 for aRFF-AP, N = 25 for aRFF-APH). Out of 7721 onset instances (see Figure 5b), the aRFF-APH algorithm correctly identified the boundary cycle in 80.0% of instances (N = 6170).