## 3 Results  
### Laboratory Results: Accuracy of Subglottal Pressure Estimation Using Four Methods  
Table 4 lists the mean and standard deviation of the RMSE within each participant group for each of the four Ps estimation methods relative to the reference intraoral pressure in the laboratory signals during bilabial closure between sustained vowels. The auditory-perceptual rating of the overall severity of the dysphonia is also reported for the patient groups as an indicator of severity of the voice disorder and whether this severity had an effect on the Ps estimation accuracy. See Appendix A for RMSE values for each study participant. Table 5 reports the results of the ANOVA analysis, revealing statistically significant main effects of the Ps estimation method and participant group. For the main effect of method, post-hoc independent-samples t-tests revealed that Estimation Methods 1 and 4 exhibited the highest error in estimating Ps, with an overall mean (standard deviation)  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Units** & **VAD Criteria** & **Description** \\ \hline Sound pressure level @ 15 cm & dB SPL & 45–130 & Acceleration amplitude mapped to acoustic sound pressure level [57] \\ \hline Fundamental frequency & Hz & 70–1000 & Reciprocal of first non-zero peak location in the normalized autocorrelation function [53] \\ \hline Autocorrelation peak amplitude & a.u. & 0.60–1 & Amplitude of first non-zero peak in the normalized autocorrelation function [77; 98] \\ \hline Subharmonic peak & a.u. & 0.25–1 & Amplitude of a secondary peak, if it exists, located between the zero-lag and the autocorrelation peak in the normalized autocorrelation function [77; 98] \\ \hline Low-to-high spectral power ratio & dB & 22–50 & Difference between spectral power below and above 2000 Hz [77] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of accelerometer-based features and voice-activity detection (VAD) range criteria for each feature computed on in-field ambulatory voice data to determine whether a 50-ms frame was considered voiced or unvoiced.  
RMSE of 3.62 (2.08) and 3.40 (1.78) cm H\({}_{2}\)O, respectively (no statistical difference, \(p\) = 0.548). Estimation Method 1 yielded outlier values for Ps for two vocally typical participants (Ps values greater than 75 H\({}_{2}\)O); these values were removed prior to computing RMSE. Lower errors were exhibited by Estimation Methods 2 and 3, which were based on participant-specific models and calibration with intraoral pressure. Estimation Method 2--the single regression model based only on ACC RMS--exhibited a statistically lower error than Estimation Method 1, with an overall RMSE of 1.81 (0.76) cm H\({}_{2}\)O (\(d\) = \(-\)1.15). Estimation Method 3--the multiple regression model incorporating the complete set of vocal function measures--exhibited the lowest overall RMSE of 1.44 (0.66) cm H\({}_{2}\)O, a further reduction in error relative to that of Estimation Method 2 (\(d\) = \(-\)0.53). Within each participant group, the mean (SD) RMSE for the PVH, NPVH, UVFP, and Control groups were, respectively, 2.74 (2.03), 2.79 (1.66), 3.36 (2.32), and 2.12 (1.19) cm H\({}_{2}\)O. For the main effect of participant group, post-hoc independent-samples t-tests revealed that the only statistically significant difference was between RMSE for the UVFP group and Control group (d = \(-\)0.69).