Publications

2015
A. F. Llico, et al., “Real-time estimation of aerodynamic features for ambulatory voice biofeedback,” The Journal of the Acoustical Society of America, vol. 138, no. 1, pp. EL14-EL19, 2015. Publisher's Version Paper
J. R. Williamson, T. F. Quatieri, B. S. Helfer, G. Ciccarelli, and D. D. Mehta, “Segment-dependent dynamics in predicting Parkinson’s disease,” Proceedings of InterSpeech, pp. 518-522, 2015. Paper
D. D. Mehta and P. J. Wolfe, “Statistical properties of linear prediction analysis underlying the challenge of formant bandwidth estimation,” The Journal of the Acoustical Society of America, vol. 137, no. 2, pp. 944-950, 2015. Publisher's Version Paper
G. Luegmair, D. D. Mehta, J. B. Kobler, and M. Döllinger, “Three-dimensional optical reconstruction of vocal fold kinematics using high-speed video with a laser projection system,” IEEE Transactions on Medical Imaging, vol. 34, no. 12, pp. 2572-2582, 2015. Publisher's Version Paper
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.

Paper
T. F. Quatieri, et al., “Vocal biomarkers to discriminate cognitive load in a working memory task,” Proceedings of InterSpeech, pp. 2684-2688, 2015. Paper
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.

Paper
2014
M. L. Cooke, D. D. Mehta, and R. E. Hillman, “Relationships between the Cepstral-Spectral Index of Dysphonia and vocal fold vibratory function during phonation,” Proceedings of the 43rd Annual Symposium of the Voice Foundation: Care of the Professional Voice, 2014. Poster
J. Guðnason, D. D. Mehta, and T. F. Quatieri, “Closed phase estimation for inverse filtering the oral airflow waveform,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 920-924, 2014.Abstract

Glottal closed phase estimation during speech production is critical to inverse filtering and, although addressed for radiated acoustic pressure analysis, must be better understood for the analysis of the oral airflow volume velocity signal that provides important properties of healthy and disordered voices. This paper compares the estimation of the closed phase from the acoustic speech signal and the oral airflow waveform recorded using a pneumotachograph mask. Results are presented for ten adult speakers with normal voices who sustained a set of vowels at a comfortable pitch and loudness. With electroglottography as reference, the identification rate and accuracy of glottal closure instants for the oral airflow are 96.8 % and 0.28 ms, whereas these metrics are 99.4 % and 0.10 ms for the acoustic signal. We conclude that glottal closure detection is adequate for close phase inverse filtering but that improvements to detection of glottal opening instants on the oral airflow signal are warranted.

Paper
D. D. Mehta, J. H. Van Stan, and R. E. Hillman, “Deriving acoustic voice quality measures from subglottal neck-surface acceleration,” Proceedings of the International Conference on Voice Physiology and Biomechanics, 2014. Poster
M. Ghassemi, et al., “Learning to detect vocal hyperfunction from ambulatory neck-surface acceleration features: Initial results for vocal fold nodules,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 6, pp. 1668-1675, 2014. Publisher's VersionAbstract

Voice disorders are medical conditions that often result from vocal abuse/misuse which is referred to generically as vocal hyperfunction. Standard voice assessment approaches cannot accurately determine the actual nature, prevalence, and pathological impact of hyperfunctional vocal behaviors because such behaviors can vary greatly across the course of an individual's typical day and may not be clearly demonstrated during a brief clinical encounter. Thus, it would be clinically valuable to develop noninvasive ambulatory measures that can reliably differentiate vocal hyperfunction from normal patterns of vocal behavior. As an initial step toward this goal we used an accelerometer taped to the neck surface to provide a continuous, noninvasive acceleration signal designed to capture some aspects of vocal behavior related to vocal cord nodules, a common manifestation of vocal hyperfunction. We gathered data from 12 female adult patients diagnosed with vocal fold nodules and 12 control speakers matched for age and occupation. We derived features from weeklong neck-surface acceleration recordings by using distributions of sound pressure level and fundamental frequency over 5-min windows of the acceleration signal and normalized these features so that intersubject comparisons were meaningful. We then used supervised machine learning to show that the two groups exhibit distinct vocal behaviors that can be detected using the acceleration signal. We were able to correctly classify 22 of the 24 subjects, suggesting that in the future measures of the acceleration signal could be used to detect patients with the types of aberrant vocal behaviors that are associated with hyperfunctional voice disorders.

Paper
R. E. Hillman, D. Mehta, J. H. Van Stan, M. Zañartu, M. Ghassemi, and J. V. Guttag, “Subglottal ambulatory monitoring of vocal function to improve voice disorder assessment,” The Journal of the Acoustical Society of America, vol. 136, pp. 2260-2260, 2014.
J. R. Williamson, T. F. Quatieri, B. S. Helfer, G. Ciccarelli, and D. D. Mehta, “Vocal and facial biomarkers of depression based on motor incoordination and timing,” Proceedings of the Fourth International Audio/Visual Emotion Challenge (AVEC 2014), 22nd ACM International Conference on Multimedia, pp. 65-72, 2014. Paper
2013
J. R. Williamson, T. F. Quatieri, B. S. Helfer, R. L. HORWITZ, B. Yu, and D. D. Mehta, “Vocal and facial biomarkers of depression based on motor incoordination,” Third International Audio/Visual Emotion Challenge (AVEC 2013), 21st ACM International Conference on Multimedia. pp. 1-4, 2013. Paper
M. Zañartu, J. C. Ho, D. D. Mehta, R. E. Hillman, and G. R. Wodicka, “Acoustic coupling during incomplete glottal closure and its effect on the inverse filtering of oral airflow,” Proceedings of Meetings on Acoustics, vol. 19, pp. 060241-7, 2013. Paper
N. Roy, et al., “Evidence-based clinical voice assessment: A systematic review,” American Journal of Speech-Language Pathology, vol. 22, pp. 212-226, 2013. Publisher's VersionAbstract

PurposeTo determine what research evidence exists to support the use of voice measures in the clinical assessment of patients with voice disorders. MethodThe American Speech-Language-Hearing Association (ASHA) National Center for Evidence-Based Practice in Communication Disorders staff searched 29 databases for peer-reviewed English-language articles between January 1930 and April 2009 that included key words pertaining to objective and subjective voice measures, voice disorders, and diagnostic accuracy. The identified articles were systematically assessed by an ASHA-appointed committee employing a modification of the critical appraisal of diagnostic evidence rating system. ResultsOne hundred articles met the search criteria. The majority of studies investigated acoustic measures (60%) and focused on how well a test method identified the presence or absence of a voice disorder (78%). Only 17 of the 100 articles were judged to contain adequate evidence for the measures studied to be formally considered for inclusion in clinical voice assessment. ConclusionResults provide evidence for selected acoustic, laryngeal imaging-based, auditory-perceptual, functional, and aerodynamic measures to be used as effective components in a clinical voice evaluation. However, there is clearly a pressing need for further high-quality research to produce sufficient evidence on which to recommend a comprehensive set of methods for a standard clinical voice evaluation.

Paper
R. E. Hillman, et al., “Future directions in the development of ambulatory monitoring for clinical voice assessment,” Proceedings of the 10th International Conference on Advances in Quantitative Laryngology, Voice and Speech Research, 2013.
D. D. Mehta, et al., “High-speed videomicroscopy and acoustic analysis of ex vivo vocal fold vibratory asymmetry,” Proceedings of the 10th International Conference on Advances in Quantitative Laryngology, Voice and Speech Research, 2013. Paper
D. D. Mehta, M. Zañartu, J. H. Van Stan, S. W. Feng, H. A. Cheyne II, and R. E. Hillman, “Smartphone-based detection of voice disorders by long-term monitoring of neck acceleration features,” Proceedings of the IEEE International Conference on Body Sensor Networks, pp. 1-6, 2013. Paper
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.

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