## 4 Discussion
### Performance of RFF Estimation Methods  
The results of this investigation indicate that using the aRFF-APH algorithm led to the greatest percentage of correctly identified boundary cycles (76.0%), followed by the aRFF-AP algorithm (70.3%) then manual estimation (20.4%). This suggests that using physiologically tuned acoustic features to identify the transition between voiced and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **RFF Estimation Methods** & _df_ & \(N\) & _X_\({}^{2}\) & \(p\) & \(V\) &
\begin{tabular}{c} **Effect Size** \\ **Interpretation** \\ \end{tabular} \\ \hline \multirow{5}{*}{Voicing Offset} & Manual vs. aRFF-AP vs. aRFF-APH & 2 & 21793 & 5821.0 & \textless{}0.001 & 0.52 & Large \\  & Manual vs. aRFF-AP & 1 & 14250 & 3928.0 & \textless{}0.001 & 0.53 & Large \\  & Manual vs. aRFF-APH & 1 & 14268 & 4982.0 & \textless{}0.001 & 0.59 & Large \\  & aRFF-AP vs. aRFF-APH & 1 & 15068 & 89.7 & \textless{}0.001 & 0.08 & Negligible \\ \hline \multirow{5}{*}{Voicing Onset} & Manual vs. aRFF-AP vs. aRFF-APH & 2 & 19112 & 6417.0 & \textless{}0.001 & 0.58 & Large \\  & Manual vs. aRFF-AP & 1 & 12631 & 3420.0 & \textless{}0.001 & 0.52 & Large \\ \cline{1-1}  & Manual vs. aRFF-APH & 1 & 12391 & 4831.0 & \textless{}0.001 & 0.62 & Large \\ \cline{1-1}  & aRFF-AP vs. aRFF-APH & 1 & 13202 & 283.0 & \textless{}0.001 & 0.15 & Small \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Chi-square (_X_\({}^{2}\)) tests of independence to examine RFF estimation method and accuracy of boundary cycle identification for voicing offset and onset. [PERSON]’s \(V\) effect sizes are interpreted using criteria from [PERSON] [49]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cohort**} & \multirow{2}{*}{_N_} & \multicolumn{3}{c}{**Proportion of Correctly Identified Boundary Cycles (\%)**} \\ \cline{3-5}  & & & **by RFF Estimation Method** \\ \cline{3-5}  & & & **Manual** & **aRFF-AP** & **aRFF-APH** \\ \hline \multirow{5}{*}{Voicing Offset} & Typical voice & 69 & 14.7 & 66.9 & 72.8 \\  & [PERSON]’s disease & 25 & 10.9 & 58.9 & 72.0 \\  & Muscle tension dysphonia & 20 & 8.3 & 66.1 & 69.0 \\  & Nodules & 4 & 7.4 & 66.3 & 69.8 \\  & Polyp & 2 & 0.0 & 53.9 & 73.1 \\  & Scarring & 1 & 20.8 & 58.3 & 87.5 \\  & Lesion & 1 & 0.0 & 53.9 & 73.1 \\ \hline \multirow{5}{*}{Voicing Onset} & Typical voice & 69 & 30.4 & 72.4 & 76.8 \\  & [PERSON]’s disease & 25 & 27.2 & 78.1 & 81.9 \\ \cline{1-1}  & Muscle tension dysphonia & 20 & 20.0 & 83.8 & 88.5 \\ \cline{1-1}  & Nodules & 4 & 15.4 & 93.6 & 98.0 \\ \cline{1-1}  & Polyp & 2 & 0.0 & 100.0 & 84.6 \\ \cline{1-1}  & Scarring & 1 & 33.3 & 62.5 & 83.3 \\ \cline{1-1}  & Lesion & 1 & 21.2 & 82.7 & 88.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Proportion of correctly identified boundary cycles (%) by RFF estimation method and cohort. unvoiced segments--even in the absence of methods to account for differences in voice sample characteristics (i.e., as in the aRFF-AP algorithm)--improves the correspondence between algorithmic and physiologic boundary cycles. These findings are in support of our hypothesis that incorporating features related to the onset and offset of vocal fold vibration improves the accuracy of acoustic voiced/unvoiced boundary detection. When examining the proportion of correctly identified boundary cycles across cohort, there were no obvious trends for either voicing offset or onset. Both aRFF-AP and aRFF-APH algorithms demonstrate relatively similar performance across voice disorder cohorts, with the exception of increased classification accuracy for individuals with nodules (N = 4) and polyp (N = 2). However, it is difficult to draw conclusions from these results due to the unbalanced nature of the dataset. For instance, the greatest proportion of correctly classified offset and onset boundary cycles via manual RFF estimation was for participants with scarring; however, there was only one participant with scarring included in the current dataset, totaling only 24 VCV instances.