## 4 Discussion  
The overall goal of the current line of research is a robust method for the non-invasive estimation of Ps during natural speech production that can be applied to laboratory, clinical, and ambulatory monitoring of vocal function. This effort builds upon ongoing work that advances algorithms for analyzing neck-surface vibration monitored using a smartphone platform to enable effective strategies for ambulatory voice monitoring and biofeedback [53; 74; 75; 76; 77; 78; 79; 80]. A critical missing link in the current set of ambulatory vocal function measures has been the estimation of Ps that would aid in better understanding vocal deficits associated with common voice disorders and would make possible the derivation of additional important vocal metrics (e.g., vocal efficiency [15; 36]). Distinguishing among voice modes and vocal pathologies is crucial to obtaining accurate ACC-based estimates of Ps. Subglottal impedance-based inverse filtering (for glottal airflow parameters) [50; 77] and vocal function analysis [56] were applied to compute estimates of signal quality and perturbation from the ACC signal. These ACC-based measures were used to help delineate different voice modes in vocally typical speakers and characterize disordered voice production associated with varying degrees of glottal closure, vocal fold stiffness, and vocal fold aduductory forces in patients with three types of voice disorders. From a previous study of ten vocally typical adults in multiple pitch and vowel contexts, the coefficient of determination was significantly higher between ACC RMS and Ps (\(r^{2}\) = 0.68-0.93) than between ACC RMS and acoustic SPL (\(r^{2}\) = 0.46-0.81) [42]. These results suggested that a linear model fit between ACC RMS and Ps could map the ACC signal onto Ps in a time-varying manner. Later work found that the mean (standard deviation) coefficient of determination between ACC RMS and Ps in a group of 26 vocally typical speakers was \(r^{2}\) = 0.72 (0.14) [63], an average RMSE of 1.7 cm H\({}_{2}\)O [44]. When non-modal phonation was elicited from the speakers with typical voices, the error in estimating Ps using ACC RMS only increased to 2.9 cm H\({}_{2}\)O [63]. The current work confirms that a multiple regression model (Estimation Method 3) performs with the highest Ps estimation accuracy relative to alternative methods by incorporating ACC RMS, \(f_{o}\), CPP, and glottal airflow measures. Ps estimation error for vocally typical speakers was thus reduced to 1.13 cm H\({}_{2}\)O on average. Ps estimation errors in the patient groups reached minimum values of 1.61, 2.08, and 1.75 cm H\({}_{2}\)O in the PVH, NPVH, and UVFP groups, respectively. Thus, in terms of accuracy, Estimation Method 3 outperformed the three alternative methods compared in this study. It is worth noting that the most recent Ps estimation method proposed in the literature (Ps Estimation Method 4 [46]) has the advantage of estimating additional measures of phonatory physiology, such as the activation of the throarytenoid muscle, cricothyroid muscle, and collision pressure of the vocal folds, but which are out of the scope of the present study. Moreover, Ps Estimation Method 4 was developed as a pilot idea designed  
Figure 6: Split-violin plots comparing laboratory (left distribution) and ambulatory (right distribution) estimates of Ps within each participant group using Ps Estimation Method 3.  
for vocally typical female voices producing /pae/-syllable tokens at different loudness conditions with comfortable pitch only; therefore, no males, different pitch conditions, or pathological voices were considered in that study. This is in agreement with the RMSE results for vocally typical participants for Ps Estimation Method 4, which is the lowest error relative to the error in the patient groups.