## 3 Results
### Ambulatory Results: Feasibility of Subglottal Pressure Estimation during Daily Life  
Since the lowest Ps estimation error was exhibited by Estimation Method 3 (multiple regression model), ambulatory estimates of Ps were computed using Ps Estimation Method 3 for each study participant's monitored day. For each participant, the multiple regression model (out of the five tested in the cross-validation) that exhibited the lowest RMSE was selected to be applied to 50-ms voiced frames in the ambulatory data signal. Figure 5 displays an example analysis of the daylong voice-use profile of participant CF3, showing the time-varying contours of each vocal function measure, with the feasibility of ambulatory Ps estimation being reported for the first time in this study.  
Table 7 reports the daylong summary statistics of the central tendency, dispersion, minimum, and maximum for the subglottal pressure and typically computed ambulatory vocal function and behavior (phonon time, SPL, CPP, and H1-H2). These ambulatory metrics have been studied in the pathophysiology and treatment of phonotraumatic and non-phonotraumatic vocal hyperfunction [101; 102; 103; 104; 105]. Summary statistics of the Ps estimates (Ps Estimation Method 3 reported) are now available to be added to the set of ambulatory vocal function measures as a key indicator of aerodynamic voice assessment. Ambulatory Ps values did not approximate statistically normal distributions; thus, the statistical mode was also reported for Ps, which resulted in values of 9.2, 8.1, 5.8, and 6.1 cm H2O for the participants with PVH, NPVH, UVFP, and typical voices. Since ambulatory estimates of glottal airflow features were input into the Ps estimation method, Table 8 documents the ambulatory statistics of these features for each study participant group.  
Figure 4 displays the probability density functions for ambulatory Ps for each participant group to investigate the ability of real-world monitoring of Ps to discriminate among patient groups and vocally typical speakers. As expected, patients with UVFP displayed the lowest average Ps during daily life, with vocally typical individuals exhibiting the next highest Ps values, followed by patients with NPVH and patients with PVH.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Group** & **RMS** & **CPP** & \(f_{\mathbf{o}}\) & **ACFL** & **MFDR** & **OQ** & **NAQ** & **HRF** & **H1–H2** \\ \hline PVH (\(n=10\)) & 9 (90\%) & 6 (60\%) & 9 (90\%) & 4 (40\%) & 7 (70\%) & 5 (50\%) & 5 (50\%) & 2 (20\%) & 4 (40\%) \\ NPVH (\(n=10\)) & 8 (80\%) & 7 (70\%) & 6 (60\%) & 5 (50\%) & 5 (50\%) & 5 (50\%) & 3 (30\%) & 4 (40\%) & 3 (30\%) \\ UVFP (\(n=10\)) & 100\%) & 2 (20\%) & 6 (60\%) & 4 (40\%) & 7 (70\%) & 2 (20\%) & 4 (40\%) & 4 (40\%) & 4 (40\%) \\ Control (\(n=26\)) & 25 (96\%) & 16 (62\%) & 18 (69\%) & 11 (42\%) & 13 (50\%) & 10 (38\%) & 10 (38\%) & 13 (50\%) & 14 (54\%) \\ \hline Average (\%) & 92\% & 53\% & 70\% & 43\% & 60\% & 40\% & 40\% & 38\% & 41\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: For each participant group, the average inclusion frequency (count and percentage) is reported for each vocal function measure that was input into Ps Estimation Method 3 (multiple regression model).  
Figure 4: Ambulatory subglottal pressure probability density for each participant group using Ps Estimation Method 3.  
Figure 5: Illustration of the time-varying nature of daylong vocal function. The first plot shows the percent phonation computed over 5-min windows at intervals of half a minute. Subsequent plots are the 5-min moving averages of the median (blue line) and the 95 th percentile (grey line) of the vocal function measure. Daylong histograms of each measure are shown to the right of each respective time series.