## 2 Materials and Methods
### Ambulatory Data Analysis  
Initial pre-processing of the accelerometer signal was required to perform voice-activity detection using previously established methods that sought to capture phonation during daily activities and avoid non-phonatory signal artifacts (e.g., tapping, clothing rubbing on sensor, non-phonatory vibrations, and electrical noise) [77]. Table 3 lists the five features and voicing criteria needed for voice-activity detection. All features were computed over 50-ms, nonoverlapping frames. If all five features were within their respective voicing range criteria, the frame was considered voiced; otherwise, the frame was considered unvoiced. For each voiced frame, we computed the set of ten vocal function measures described in Table 2 for each study participant's day of voice monitoring.  
Since direct measurements of acoustic SPL were not available from the ambulatory voice monitor, estimates of SPL were derived from a mapping between the accelerometer and microphone recordings of an /ah/ vowel of decreasing loudness at the beginning of each participant's monitored day [53]. Linear regression parameters were computed in a log-log space between measures of accelerometer RMS and acoustic SPL, as specified in previous studies [57]. In this manner, participant-specific slope and intercept parameters were saved and applied to the ambulatory accelerometer signal (when the microphone was not present) to map the accelerometer level (dB re 1 cm/s2) to units of dB SPL @ 15 cm.  
As with the laboratory accelerometer data, the ambulatory accelerometer signals were calibrated to physical units of vibration acceleration (cm/s\({}^{2}\)) using the respective sensor's derived calibration factor. This calibration allowed for the application of subglottal impedance-based inverse filtering to derive an estimate of the (zero-mean) glottal airflow waveform for each voiced frame. In contrast with the laboratory data analysis where oral airflow recordings were available, the ambulatory ACC signal needed to be processed using a single optimized IBIF inverse filter that was considered time-invariant and specific to each study participant to account for skin properties, tracheal geometry, and ACC sensor placement. The IBIF model was selected from a laboratory vowel /a/ segment with the highest subglottal pressure in the comfortable pitch condition (and modal voice quality for the vocally typical group). The assumption of IBIF model time-invariance was based on the model properties, which were assumed to be stable over time [50]. Even though there is some evidence that some of the neck-skin properties might change for different articulatory configurations (e.g., glottal flow estimation from an /a/ vowel compared to an /i/ vowel [99], the extent of the effect is not significant for ambulatory purposes [100]).  
Thus, glottal airflow features were able to be estimated in the ambulatory setting as in prior work [96]; in the current study, these features were used to aid in accurate estimation of Ps. As with the laboratory data analysis, voiced frames with IBIF measures that were outside physiologically relevant ranges were not included for Ps estimation (ACFL < 1 mL/s, MFDR < 1 L/s\({}^{2}\), OQ outside of 0-100% range, and \(f_{\text{o}}\) > 500 Hz). In this paper, estimates of ambulatory Ps are reported using Ps Estimation Method 3, which was found to yield the lowest error among the four methods compared according to the laboratory results. The regression model of Estimation Method 3 was selected from the participant-specific laboratory training data that yielded the lowest test-set RMSE.