## 3 Results
### Performance of Manual and Semi-Automated RFF Estimation Methods
The aRFF-AP algorithm correctly identified the boundary cycle in 77.2% of onset instances (N = 5833), followed by manual estimation in which 28.0% of onset instances (N = 2158) resulted in correctly identified boundary cycles. Misclassifications occurred at the rate of 4.0% for aRFF-APH (N = 310), 11.5% for aRFF-AP (N = 888), and 48.6% for manual RFF estimation (N = 3752). Almost a quarter (N = 1811) of onset instances were rejected during manual analysis. The aRFF-AP algorithm led to the least number of rejected onset instances (N = 1000; 13.0%), followed by the aRFF-APH algorithm (N = 1241; 16.1%). A total of 216 onset instances were automatically rejected by the aRFF-AP and aRFF-APH algorithms due to a pitch strength < 0.05; the remainder of these rejections were due to errors in identifying voiced cycles (N = 567 for aRFF-AP, N = 891 for aRFF-APH), or post-processing of resulting RFF values (N = 217 for aRFF-AP, N = 134 for aRFF-APH). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Acoustic Feature**} & \multirow{2}{*}{**Coef**} & \multirow{2}{*}{**SE Coef**} & \multirow{2}{*}{\(\mathbf{z}\)} & \multirow{2}{*}{\(\mathbf{p}\)} & \multicolumn{2}{c}{**95\% Confidence Interval**} & \multirow{2}{*}{**VIF 1**} \\ \cline{5-8}  & & & & & & & **Lower Bound** & **Upper Bound** \\ \hline \multirow{8}{*}{Voicing Offset} & Constant & 0.10 & 0.07 & 1.48 & 0.15 & \(-\)0.03 & 0.24 & — \\  & Filtered Waveform Shape Similarity & \(-\)1.52 & 0.05 & \(-\)30.07 & \(<\)0.001 & \(-\)1.62 & \(-\)1.42 & 1.30 \\  & Median of Voice \(f_{o}\) & 1.46 & 0.04 & 34.85 & \(<\)0.001 & 1.37 & 1.54 & 1.21 \\  & Cepstral Peak Prominence & 1.23 & 0.06 & 20.07 & \(<\)0.001 & 1.11 & 1.35 & 1.27 \\  & Number of Zero Crossings & \(-\)3.31 & 0.04 & \(-\)78.69 & \(<\)0.001 & \(-\)3.39 & \(-\)3.23 & 1.55 \\  & Short-Time Energy & \(-\)5.72 & 0.15 & \(-\)38.18 & \(<\)0.001 & \(-\)6.01 & \(-\)5.42 & 9.03 \\  & Average Pitch Strength & 9.24 & 0.12 & 78.52 & \(<\)0.001 & 9.01 & 9.47 & 4.81 \\  & Normalized Cross-Correlation & \(-\)0.84 & 0.05 & \(-\)16.77 & \(<\)0.001 & \(-\)0.93 & \(-\)0.74 & 1.53 \\  & Cross-Correlation & 1.00 & 0.16 & 6.25 & \(<\)0.001 & 0.69 & 1.31 & 7.74 \\ \hline \multirow{8}{*}{Voicing Onset} & Constant & \(-\)2.18 & 0.10 & \(-\)22.69 & \(<\)0.001 & \(-\)2.37 & \(-\)2.00 & — \\  & Filtered Waveform Shape Similarity & 1.40 & 0.08 & 18.34 & \(<\)0.001 & 1.25 & 1.95 & 1.30 \\  & Median of Voice \(f_{o}\) & 2.21 & 0.06 & 40.31 & \(<\)0.001 & 2.10 & 2.31 & 1.19 \\  & Cepstral Peak Prominence & 1.05 & 0.08 & 12.53 & \(<\)0.001 & 0.89 & 1.22 & 1.06 \\  & Number of Zero Crossings & \(-\)2.62 & 0.06 & \(-\)42.15 & \(<\)0.001 & \(-\)2.75 & \(-\)2.50 & 1.66 \\  & Average Pitch Strength & 8.94 & 0.15 & 59.45 & \(<\)0.001 & 8.65 & 9.24 & 2.83 \\  & Signal-to-Noise Ratio & 0.56 & 0.06 & 9.84 & \(<\)0.001 & 0.45 & 0.68 & 2.44 \\  & Filtered Short-Time Energy & \(-\)3.75 & 0.10 & \(-\)37.51 & \(<\)0.001 & \(-\)3.95 & \(-\)3.56 & 3.66 \\  & Filtered Short-Time Log Energy & 3.11 & 0.07 & 44.81 & \(<\)0.001 & 2.97 & 3.24 & 3.01 \\ \hline \hline \end{tabular}  
* VIF = variable inflation factor.  
\end{table} 
Table 4: Summary of significant variables in the stepwise binary logistic regression statistical model. The results of the chi-square tests are shown in Table 6. Boundary cycle classification accuracy was significantly different for RFF estimation method, producing large effect sizes for both voicing offset (\(p<0.001\), \(V=0.52\)) and onset (\(p<0.001\), \(V=0.58\)). Boundary cycle classification accuracy was significantly different between manual and aRFF-AP methods. Post hoc analyses revealed a large effect for voicing offset (\(p<0.001\), \(V=0.53\)) and onset (\(p<0.001\), \(V=0.52\)), wherein aRFF-AP was more likely to correctly identify the boundary cycle than manual estimation. Boundary cycle classification accuracy was also significantly different between manual and aRFF-APH methods.