Publications by Year: 2014

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