Ambulatory Monitoring of Subglottal Pressure Estimated from Neck-Surface Vibration in Individuals with and without Voice Disorders

The aerodynamic voice assessment of subglottal air pressure can discriminate between speakers with typical voices from patients with voice disorders, with further evidence validating subglottal pressure as a clinical outcome measure. Although estimating subglottal pressure during phonation is an important component of a standard voice assessment, current methods for estimating subglottal pressure rely on non-natural speech tasks in a clinical or laboratory setting. This study reports on the validation of a method for subglottal pressure estimation in individuals with and without voice disorders that can be translated to connected speech to enable the monitoring of vocal function and behavior in real-world settings. During a laboratory calibration session, a participant-specific multiple regression model was derived to estimate subglottal pressure from a neck-surface vibration signal that can be recorded during natural speech production. The model was derived for vocally typical individuals and patients diagnosed with phonotraumatic vocal fold lesions, primary muscle tension dysphonia, and unilateral vocal fold paralysis. Estimates of subglottal pressure using the developed method exhibited significantly lower error than alternative methods in the literature, with average errors ranging from 1.13 to 2.08 cm H₂O for the participant groups. The model was then applied during activities of daily living, thus yielding ambulatory estimates of subglottal pressure for the first time in these populations. Results point to the feasibility and potential of real-time monitoring of subglottal pressure during an individual's daily life for the prevention, assessment, and treatment of voice disorders.

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

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 H2O) and 243 Pa (2.48 cm H2O), 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.

Improved subglottal pressure estimation from neck-surface vibration in healthy speakers producing non-modal phonation

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 (/ɑ/, /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 multidimensional 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.

Relationships between vocal function measures derived from an acoustic microphone and a subglottal neck-surface accelerometer

Monitoring subglottal neck-surface acceleration has received renewed attention due to the ability of low-profile accelerometers to confidentially and noninvasively track properties related to normal and disordered voice characteristics and behavior. This study investigated the ability of subglottal neck-surface acceleration to yield vocal function measures traditionally derived from the acoustic voice signal and help guide the development of clinically functional accelerometer-based measures from a physiological perspective. Results are reported for 82 adult speakers with voice disorders and 52 adult speakers with normal voices who produced the sustained vowels /a/, /i/, and /u/ at a comfortable pitch and loudness during the simultaneous recording of radiated acoustic pressure and subglottal neck-surface acceleration. As expected, timing-related measures of jitter exhibited the strongest correlation between acoustic and neck-surface acceleration waveforms (r ≤ 0.99), whereas amplitude-based measures of shimmer correlated less strongly (r ≤ 0.74). Additionally, weaker correlations were exhibited by spectral measures of harmonics-to-noise ratio (r ≤ 0.69) and tilt (r ≤ 0.57), whereas the cepstral peak prominence correlated more strongly (r ≤ 0.90). These empirical relationships provide evidence to support the use of accelerometers as effective complements to acoustic recordings in the assessment and monitoring of vocal function in the laboratory, clinic, and during an individual's daily activities.