## 3 Results
### Acoustic Feature Set Reduction  
The results of the logistic regression (shown in Table 4) indicated that filtered waveform shape similarity, median of voice \(f_{o}\), cepstral peak prominence, number of zero crossings, short-time energy, average pitch strength, normalized cross-correlation, and cross-correlation were all significant predictors of voicing status for voicing offset (\(p<0.05\)). When using these eight features, the model for voicing offset accounted for 61.7% of the variance in voicing status (adjusted \(R^{2}=61.7\%\)), with an area under the receiver operating characteristic (ROC) curve of 0.96. Inspection of the coefficients indicated that the log odds of voicing decreased per one-unit increase in short-time energy, normalized cross-correlation, number of zero crossings, or filtered waveform shape similarity (i.e., negative coefficient). On the other hand, the log odds of voicing increased per one-unit increase in median of voice \(f_{o}\), cepstral peak prominence, average pitch strength, or cross-correlation (i.e., positive coefficient).  
For voicing onset, the stepwise binary logistic regression revealed that filtered waveform shape similarity, median of voice \(f_{o}\), cepstral peak prominence, number of zero crossings, average pitch strength, signal-to-noise ratio, filtered short-time energy, and filtered short-time log energy were all significant predictors of voicing status (\(p<0.05\); see Table 4). The model for voicing onset accounted for 75.8% of the variance in voicing status (adjusted \(R^{2}=75.8\%\)), with an area under the ROC curve of 0.98. The model for voicing onset indicated that the log odds of voicing decreased per one-unit increase in  
Figure 3: Normalized feature values calculated from the raw microphone signal or Auditory-SWIPE\({}^{\prime}\) output (teal) with respect to distance (pitch periods) from the true boundary cycle (thin black dotted line) for voicing offset. 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 \(f_{o}\) (\(A_{f_{o}}\), \(M_{f_{o}}\), 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.  
number of zero crossings or filtered short-time energy. The log odds of voicing increased per-unit increase in filtered waveform shape similarity, median of voice \(f_{o}\), cepstral peak prominence, average pitch strength, signal-to-noise ratio, or filtered short-time log energy. The resulting acoustic features were then incorporated into the aRFF-APH algorithms to identify the boundary cycle of voicing.