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.
    K. L. Marks, J. Z. Lin, A. Fox, L. E. Toles, and D. D. Mehta, “Impact of non-modal phonation on estimates of subglottal pressure from neck-surface acceleration in healthy speakers,” Journal of Speech, Language, and Hearing Research, vol. 62, no. 9, pp. 3339-3358, 2019. Publisher's VersionAbstract


    The purpose of this study was to evaluate the effects of nonmodal phonation on estimates of subglottal pressure (Ps) derived from the magnitude of a neck-surface accelerometer (ACC) signal and to confirm previous findings regarding the impact of vowel contexts and pitch levels in a larger cohort of participants.


    Twenty-six vocally healthy participants (18 women, 8 men) were asked to produce a series of p-vowel syllables with descending loudness in 3 vowel contexts (/a/, /i/, and /u/), 3 pitch levels (comfortable, high, and low), and 4 elicited phonatory conditions (modal, breathy, strained, and rough). Estimates of Ps for each vowel segment were obtained by averaging the intraoral air pressure plateau before and after each segment. The root-mean-square magnitude of the neck-surface ACC signal was computed for each vowel segment. Three linear mixed-effects models were used to statistically assess the effects of vowel, pitch, and phonatory condition on the linear relationship (slope and intercept) between Ps and ACC signal magnitude.


    Results demonstrated statistically significant linear relationships between ACC signal magnitude and Ps within participants but with increased intercepts for the nonmodal phonatory conditions; slopes were affected to a lesser extent. Vowel and pitch contexts did not significantly affect the linear relationship between ACC signal magnitude and Ps.


    The classic linear relationship between ACC signal magnitude and Ps is significantly affected when nonmodal phonation is produced by a speaker. Future work is warranted to further characterize nonmodal phonatory characteristics to improve the ACC-based prediction of Ps during naturalistic speech production.

    J. P. Cortés, et al., “Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration,” PLoS One, vol. 13, no. 12, pp. e0209017, 2018. Publisher's VersionAbstract
    Phonotraumatic vocal hyperfunction (PVH) is associated with chronic misuse and/or abuse of voice that can result in lesions such as vocalfold nodules. The clinical aerodynamic assessment of vocal function has been recently shown to differentiate between patients with PVH and healthy controls to provide meaningful insight into pathophysiological mechanisms associated with these disorders. However, all current clinical assessment of PVH is incomplete because of its inability to objectively identify the type and extent of detrimental phonatory function that is associated with PVH during daily voice use. The current study sought to address this issue by incorporating, for the first time in a comprehensive ambulatory assessment, glottal airflow parameters estimated from a neck-mounted accelerometer and recorded to a smartphone-based voice monitor. We tested this approach on 48 patients with vocal fold nodules and 48 matched healthy-control subjects who each wore the voice monitor for a week. Seven glottal airflow features were estimated every 50 ms using an impedance-based inverse filtering scheme, and seven high-order summary statistics of each feature were computed every 5 minutes over voiced segments. Based on a univariate hypothesis testing, eight glottal airflow summary statistics were found to be statistically different between patient and healthy-control groups. L1-regularized logistic regression for a supervised classification task yielded a mean (standard deviation) area under the ROC curve of 0.82 (0.25) and an accuracy of 0.83 (0.14). These results outperform the state-of-the-art classification for the same classification task and provide a new avenue to improve the assessment and treatment of hyperfunctional voice disorders.
    A. S. Fryd, J. H. Van Stan, R. E. Hillman, and D. D. Mehta, “Estimating subglottal pressure from neck-surface acceleration during normal voice production,” Journal of Speech, Language, and Hearing Research, vol. 59, no. 6, pp. 1335-1345, 2016. Publisher's VersionAbstract

    Purpose The purpose of this study was to evaluate the potential for estimating subglottal air pressure using a neck-surface accelerometer and to compare the accuracy of predicting subglottal air pressure relative to predicting acoustic sound pressure level (SPL).

    Method Indirect estimates of subglottal pressure (Psg′) were obtained from 10 vocally healthy speakers during loud-to-soft repetitions of 3 different /p/–vowel gestures (/pa/, /pi/, /pu/) at 3 pitch levels in the modal register. Intraoral air pressure, neck-surface acceleration, and radiated acoustic pressure were recorded, and the root-mean-square amplitude of the acceleration signal was correlated with Psg′ and SPL.

    Results The coefficient of determination between accelerometer level and Psg′ was high when data were pooled from all vowel and pitch contexts for each participant (r 2 = .68–.93). These relationships were stronger than corresponding relationships between accelerometer level and SPL (r 2 = .46–.81). The average 95% prediction interval for estimating Psg′ using accelerometer level was ±2.53 cm H2O, ranging from ±1.70 to ±3.74 cm H2O across participants.

    Conclusions Accelerometer signal amplitude correlated more strongly with Psg′ than with SPL. Future work is warranted to investigate the robustness of the relationship in nonmodal voice qualities, individuals with voice disorders, and accelerometer-based ambulatory monitoring of subglottal pressure.

    D. Mehta, J. Van Stan, and R. Hillman, “Relationships between vocal function measures derived from an acoustic microphone and a subglottal neck-surface accelerometer,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 4, pp. 659-668, 2016. Publisher's VersionAbstract

    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 necksurface 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 necksurface acceleration. As expected, timing-related measures of jitter exhibited the strongest correlation between acoustic and necksurface acceleration waveforms (r 0:99), whereas amplitudebased 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.

    Y. - A. S. Lien, et al., “Voice relative fundamental frequency via neck-skin acceleration in individuals with voice disorders,” Journal of Speech, Language, and Hearing Research, vol. 58, no. 5, pp. 1482-1487, 2015. Publisher's VersionAbstract

    Abstract Purpose: This study investigated the use of neck-skin acceleration for relative fundamental frequency (RFF) analysis. Method: Forty individuals with voice disorders associated with vocal hyperfunction and 20 age- and sex-matched control participants were recorded with a subglottal neck-surface accelerometer and a microphone while producing speech stimuli appropriate for RFF. Rater reliabilities, RFF means, and RFF standard deviations derived from the accelerometer were compared with those derived from the microphone. Results: RFF estimated from the accelerometer had slightly higher intrarater reliability and identical interrater reliability compared with values estimated with the microphone. Although sensor type and the Vocal Cycle × Sensor and Vocal Cycle × Sensor × Group interactions showed significant effects on RFF means, the typical RFF pattern could be derived from either sensor. For both sensors, the RFF of individuals with vocal hyperfunction was lower than that of the controls. Sensor type and its interactions did not have significant effects on RFF standard deviations. Conclusions: RFF can be reliably estimated using an accelerometer, but these values cannot be compared with those collected via microphone. Future studies are needed to determine the physiological basis of RFF and examine the effect of sensors on RFF in practical voice assessment and monitoring settings.

    D. D. Mehta, et al., “Using ambulatory voice monitoring to investigate common voice disorders: Research update,” Frontiers in Bioengineering and Biotechnology, vol. 3, no. 155, pp. 1-14, 2015. Publisher's VersionAbstract

    Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, referred to as vocal hyperfunction. The clinical management of hyperfunctional voice disorders would be greatly enhanced by the ability to monitor and quantify detrimental vocal behaviors during an individual’s activities of daily life. This paper provides an update on ongoing work that uses a miniature accelerometer on the neck surface below the larynx to collect a large set of ambulatory data on patients with hyperfunctional voice disorders (before and after treatment) and matched-control subjects. Three types of analysis approaches are being employed in an effort to identify the best set of measures for differentiating among hyperfunctional and normal patterns of vocal behavior: (1) ambulatory measures of voice use that include vocal dose and voice quality correlates, (2) aerodynamic measures based on glottal airflow estimates extracted from the accelerometer signal using subject-specific vocal system models, and (3) classification based on machine learning and pattern recognition approaches that have been used successfully in analyzing long-term recordings of other physiological signals. Preliminary results demonstrate the potential for ambulatory voice monitoring to improve the diagnosis and treatment of common hyperfunctional voice disorders.

    M. Zañartu, J. C. Ho, D. D. Mehta, R. E. Hillman, and G. R. Wodicka, “Subglottal impedance-based inverse filtering of voiced sounds using neck surface acceleration,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, pp. 1929-1939, 2013.Abstract

    A model-based inverse filtering scheme is proposed for an accurate, non-invasive estimation of the aerodynamic source of voiced sounds at the glottis. The approach, referred to as subglottal impedance-based inverse filtering (IBIF), takes as input the signal from a lightweight accelerometer placed on the skin over the extrathoracic trachea and yields estimates of glottal airflow and its time derivative, offering important advantages over traditional methods that deal with the supraglottal vocal tract. The proposed scheme is based on mechano-acoustic impedance representations from a physiologically-based transmission line model and a lumped skin surface representation. A subject-specific calibration protocol is used to account for individual adjustments of subglottal impedance parameters and mechanical properties of the skin. Preliminary results for sustained vowels with various voice qualities show that the subglottal IBIF scheme yields comparable estimates with respect to current aerodynamics-based methods of clinical vocal assessment. A mean absolute error of less than 10% was observed for two glottal airflow measures—maximum flow declination rate and amplitude of the modulation component—that have been associated with the pathophysiology of some common voice disorders caused by faulty and/or abusive patterns of vocal behavior (i.e., vocal hyperfunction). The proposed method further advances the ambulatory assessment of vocal function based on the neck acceleration signal, that previously have been limited to the estimation of phonation duration, loudness, and pitch. Subglottal IBIF is also suitable for other ambulatory applications in speech communication, in which further evaluation is underway.