### Subglottal Pressure Estimation from Anterior Neck-Surface Vibration  
Recent lines of research have focused on estimating Ps from anterior neck-surface vibration using a miniature accelerometer (ACC) placed below the level of the glottis [42; 43; 44; 45]. This ACC sensor is a piezo-ceramic vibration transducer that measures the second derivative (acceleration) of the one-dimensional displacement perpendicular to the surface of the neck skin. Monitoring vocal characteristics using ACC sensors is desirable because these sensors have been shown to be robust to airborne acoustic noise relative to contact microphones [47; 48; 49], produce a voice-related signal that is not filtered by vocal tract resonances and thus unintelligible (maintaining confidentiality) [50], and can be part of wearable systems for long-term ambulatory voice monitoring [51; 52; 53]. Positioning the ACC sensor below the glottis enables measurement of Ps-related information due to coupling of aerodynamic pressures in the trachea through the tracheal and neck tissue to the surface of the skin [54; 55]. Amplitude and frequency properties of the subglottal ACC signal have been shown to correlate highly with properties of the associated acoustic voice signal, including \(f_{\mathrm{o}}\) and variability metrics such as jitter and cepstral peak prominence (CPP) [56]. In fact, the root-mean-square (RMS) value of the ACC signal has been used as the primary correlate of acoustic sound pressure level (SPL) through simple linear mapping [57]; when the phonatory SPL increases, the RMS magnitude of the ACC signal generally increases as well. This mapping approximately holds across loudness and pitch contexts and can be used as a calibration step so that the SPL and derived vocal dose measures can be derived from the ACC signal in ambulatory contexts [58; 59]. ACC-derived measures of SPL and \(f_{\mathrm{o}}\) can then be input into an empirical formula found in the literature to estimate Ps [58; 60; 61]. Using this approach, the derivation of ACC-based Ps is applied on a person-specific basis since the RMS-based mapping to SPL is not universal and depends on the variability in neck tissue morphology and acoustic-aerodynamic relations across individuals [57]. The accuracy of estimating Ps in this manner is thus dependent on the validity of the model, as well as the accuracy in estimating SPL and \(f_{\mathrm{o}}\). The accuracy in estimating\(f_{\mathrm{o}}\) from the ACC signal is very high [56], validating why ACC signals have been used for noise-robust\(f_{\mathrm{o}}\) tracking for decades [49]. However, the accuracy in estimating SPL from the ACC signal is lower, with average confidence intervals lying within \(\pm 6\) dB [57], which is a range spanning soft-to-loud loudness levels [62]. ACC-based estimation of SPL can also be affected by other factors such as vocal tract shape (vowel type) and glottal configuration (leading to different voice qualities). For example, evidence from vocally typical speakers points to higher correlations between ACC RMS and Ps than between ACC RMS and SPL when investigating the impact of variations in vowel type and pitch [42]. Thus, this alternative approach to ACC-based Ps estimation bypasses the need for SPL and \(f_{\mathrm{o}}\) estimation, with the RMS value of the ACC signal acting as a person-specific correlate of Ps in modal phonation. The effects of non-modal phonation (breathiness, roughness, and strain) on the linear ACC RMS-Ps mapping were subsequently studied in vocally typical speakers [63]. Results demonstrated, as expected, a statistically significant linear relationship between ACC RMS and Ps for each speaker producing modal phonation; however, the linear model exhibited larger intercepts when non-modal phonatory conditions were elicited (slopes were less affected by non-modal phonation). In a follow-up study of patients with voice disorders, patients exhibited higher model intercepts; i.e., higher levels of Ps given similar ACC RMS values when compared with vocally typical individuals [64].