## 4 Discussion
Although the triangular body-cover model has limitations in terms of the \(f_{\text{o}}\) range and offsets of SPL with respect to clinical data that may vary among individuals, the Ps estimation error is comparable to that of the other methods analyzed in this study for the control group. By improving Ps Estimation Method 4 with more simulations for different pathological voice cases, a more robust implementation for estimating Ps for general cases could be obtained, without the necessity of individual models for each speaker (except for individual IBIF models that are still needed to extract aerodynamic measures as input to the neural network). Although Estimation Method 3 yields the highest Ps estimation accuracy, application of the model requires the computation of several vocal function measures that may each be prone to their own estimation uncertainty. In particular, the IBIF-related glottal airflow measures were only considered valid if they were within physiologically relevant ranges and associated with \(f_{\text{o}}\) values less than 500 Hz. Thus, voiced frames outside of these ranges could not yield glottal airflow measures and, by definition, could not be analyzed using the Ps estimation methods (Estimation Methods 3 and 4) that required these measures as input. This limitation can restrict certain application areas; further work is needed to study scenarios known to exhibit phonation at very high pitch values, including singing voice, infant-directed speech, and pediatric voices. For these scenarios, it would be reasonable to apply Ps Estimation Method 2, which only requires the computation of ACC RMS for input into a single, person-specific regression model. For many study participants, the Ps estimation error for Estimation Method 2 was similar to that exhibited by Estimation Method 3. In addition, the simpler regression model of Estimation Method 2 could be more easily implemented for real-time estimation of Ps as part of a wearable voice monitoring and biofeedback system. Placed in clinical context, the Ps estimation errors obtained in this study were smaller than known differences in Ps between patients with voice disorders and vocally typical controls. Differences in Ps have been reported to be in the range of 4-5 cm H\({}_{2}\)O for the discrimination of patients with PVH from vocally typical speakers [70]. Furthermore, the strong discriminatory power between patients and controls has been shown to be maintained with ACC-based Ps estimation using Estimation Method 2 ([PERSON]'s \(d\) effect sizes up to 1.63) [45]. Reductions in Ps can reach up to 13 cm H\({}_{2}\)O following laryngeal surgery to improve glottal closure for patients with UVFP [71] and laryngeal cancer [24]. Thus, the errors in estimating ACC-based Ps using neck-surface vibration are low enough to use Ps for clinical voice assessment. Calibrating the ACC signal for Ps can also yield an interpretable voice source measure, in contrast to SPL, which is an acoustic measure sensitive to effects of articulation. In terms of ambulatory voice monitoring, significant progress has been made to characterize patients with PVH who tend to speak with a more restricted pitch range (reduction in \(f_{\text{o}}\) variation), a louder voice more often (the SPL distribution skews toward higher values), and a reduced variability in glottal closure patterns (the distribution of H1-H2 is more restricted) relative to vocally typical individuals [101]. The characterization of changes in Ps promises to provide additional insight into the real-world vocal behavior of individuals with PVH or who are at risk for developing phonotrauma. It is believed that a primary contributing factor to phonotrauma is an increase in vocal fold collision forces during voice production. Since previous work has pointed to a high correlation between vocal fold collision pressure and Ps in certain phonatory scenarios [106; 107], ambulatory Ps measures could be used as surrogates for vocal fold collision.