E. J. Ibarra, et al., “Estimation of subglottal pressure, vocal fold collision pressure, and intrinsic laryngeal muscle activation from neck-surface vibration using a neural network framework and a voice production model,” Frontiers in Physiology, vol. 12, no. 732244, 2021. Publisher's VersionAbstract
    The ambulatory assessment of vocal function can be significantly enhanced by having access to physiologically based features that describe underlying pathophysiological mechanisms in individuals with voice disorders. This type of enhancement can improve methods for the prevention, diagnosis, and treatment of behaviorally based voice disorders. Unfortunately, the direct measurement of important vocal features such as subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation is impractical in laboratory and ambulatory settings. In this study, we introduce a method to estimate these features during phonation from a neck-surface vibration signal through a framework that integrates a physiologically relevant model of voice production and machine learning tools. The signal from a neck-surface accelerometer is first processed using subglottal impedance-based inverse filtering to yield an estimate of the unsteady glottal airflow. Seven aerodynamic and acoustic features are extracted from the neck surface accelerometer and an optional microphone signal. A neural network architecture is selected to provide a mapping between the seven input features and subglottal pressure, vocal fold collision pressure, and cricothyroid and thyroarytenoid muscle activation. This non-linear mapping is trained solely with 13,000 Monte Carlo simulations of a voice production model that utilizes a symmetric triangular body-cover model of the vocal folds. The performance of the method was compared against laboratory data from synchronous recordings of oral airflow, intraoral pressure, microphone, and neck-surface vibration in 79 vocally healthy female participants uttering consecutive /pæ/ syllable strings at comfortable, loud, and soft levels. The mean absolute error and root-mean-square error for estimating the mean subglottal pressure were 191 Pa (1.95 cm H(2)O) and 243 Pa (2.48 cm H(2)O), respectively, which are comparable with previous studies but with the key advantage of not requiring subject-specific training and yielding more output measures. The validation of vocal fold collision pressure and laryngeal muscle activation was performed with synthetic values as reference. These initial results provide valuable insight for further vocal fold model refinement and constitute a proof of concept that the proposed machine learning method is a feasible option for providing physiologically relevant measures for laboratory and ambulatory assessment of vocal function.
    K. L. Marks, J. Z. Lin, J. A. Burns, T. A. Hron, R. E. Hillman, and D. D. Mehta, “Estimation of subglottal pressure from neck surface vibration in patients with voice disorders,” Journal of Speech, Language, and Hearing Research, vol. 63, no. 7, pp. 2202-2218, 2020. Publisher's VersionAbstract


    Given the established linear relationship between neck surface vibration magnitude and mean subglottal pressure (Ps) in vocally healthy speakers, the purpose of this study was to better understand the impact of the presence of a voice disorder on this baseline relationship.


    Data were obtained from participants with voice disorders representing a variety of glottal conditions, including phonotraumatic vocal hyperfunction, nonphonotraumatic vocal hyperfunction, and unilateral vocal fold paralysis. Participants were asked to repeat /p/-vowel syllable strings from loud-to-soft loudness levels in multiple vowel contexts (/pa/, /pi/, /pu/) and pitch levels (comfortable, higher than comfortable, lower than comfortable). Three statistical metrics were computed to analyze the regression line between neck surface accelerometer (ACC) signal magnitude and Ps within and across pitch, vowel, and voice disorder category: coefficient of determination (r2), slope, and intercept. Three linear mixed-effects models were used to evaluate the impact of voice disorder category, pitch level, and vowel context on the relationship between ACC signal magnitude and Ps.


    The relationship between ACC signal magnitude and Ps was statistically different in patients with voice disorders than in vocally healthy controls; patients exhibited higher levels of Ps given similar values of ACC signal magnitude. Negligible effects were found for pitch condition within each voice disorder category, and negligible-to-small effects were found for vowel context. The mean of patient-specific r2 values was .63, ranging from .13 to .92.


    The baseline, linear relationship between ACC signal magnitude and Ps is affected by the presence of a voice disorder, with the relationship being participant-specific. Further work is needed to improve ACC-based prediction of Ps, across treatment, and during naturalistic speech production.

    J. Z. Lin, V. M. Espinoza, M. Zañartu, K. L. Marks, and D. D. Mehta, “Improved subglottal pressure estimation from neck-surface vibration in healthy speakers producing non-modal phonation,” IEEE Journal of Special Topics in Signal Processing, vol. 14, no. 2, pp. 449-460, 2020. Publisher's VersionAbstract
    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.
    J. H. Van Stan, D. D. Mehta, and R. E. Hillman, “Recent innovations in voice assessment expected to impact the clinical management of voice disorders,” Perspectives of the ASHA Special Interest Groups, vol. 2, no. SIG 3, pp. 4-13, 2017. Publisher's VersionAbstract

    This article provides a summary of some recent innovations in voice assessment expected to have an impact in the next 5–10 years on how patients with voice disorders are clinically managed by speech-language pathologists. Specific innovations discussed are in the areas of laryngeal imaging, ambulatory voice monitoring, and “big data” analysis using machine learning to produce new metrics for vocal health. Also discussed is the potential for using voice analysis to detect and monitor other health conditions.