Improved Subglottal Pressure Estimation From Neck-Surface Vibration in Healthy Speakers Producing Non-Modal Phonation

• Published in: IEEE journal of selected topics in signal processing 2020-02, Vol.14 (2), p.449-460
• Main Authors: Lin, Jon Z., Espinoza, Victor M., Marks, Katherine L., Zanartu, Matias, Mehta, Daryush D.
• Format: Article
• Language: English
• Subjects: Accelerometers; Air flow; ambulatory voice monitoring; Atmospheric modeling; clinical voice assessment; Estimation; Monitoring; neck-surface accelerometer; Phonation; Pitch; Production; Regression models; Resonant frequencies; Robustness (mathematics); Sound pressure; Subglottal pressure; Surgery; Vibrations; Voice control
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2019.2959267

Subglottal air pressure plays a major role in voice production and is a primary factor in controlling voice onset, offset, sound pressure level, glottal airflow, vocal fold collision pressures, and variations in fundamental frequency. Previous work has shown promise for the estimation of subglottal pressure from an unobtrusive miniature accelerometer sensor attached to the anterior base of the neck during typical modal voice production across multiple pitch and vowel contexts. This study expands on that work to incorporate additional accelerometer-based measures of vocal function to compensate for non-modal phonation characteristics and achieve an improved estimation of subglottal pressure. Subjects with normal voices repeated /p/-vowel syllable strings from loud-to-soft levels in multiple vowel contexts (/a/, /i/, and /u/), pitch conditions (comfortable, lower than comfortable, higher than comfortable), and voice quality types (modal, breathy, strained, and rough). Subject-specific, stepwise regression models were constructed using root-mean-square (RMS) values of the accelerometer signal alone (baseline condition) and in combination with cepstral peak prominence, fundamental frequency, and glottal airflow measures derived using subglottal impedance-based inverse filtering. Five-fold cross-validation assessed the robustness of model performance using the root-mean-square error metric for each regression model. Each cross-validation fold exhibited up to a 25% decrease in prediction error when the model incorporated multi-dimensional aspects of the accelerometer signal compared with RMS-only models. Improved estimation of subglottal pressure for non-modal phonation was thus achievable, lending to future studies of subglottal pressure estimation in patients with voice disorders and in ambulatory voice recordings.