Publications by Year: 2016

M. E. Powell, D. D. Deliyski, R. E. Hillman, S. M. Zeitels, J. A. Burns, and D. D. Mehta, “Comparison of videostroboscopy to stroboscopy derived from high-speed videoendoscopy for evaluating patients with vocal fold mass lesions,” American Journal of Speech-Language Pathology, vol. 25, pp. 576-589, 2016. Publisher's VersionAbstract

indirect physiological signal to predict the phase of the vocal fold vibratory cycle for sampling. Simulated stroboscopy (SS) extracts the phase of the glottal cycle directly from the changing glottal area in the high-speed videoendoscopy (HSV) image sequence. The purpose of this study is to determine the reliability of SS relative to VS for clinical assessment of vocal fold vibratory function in patients with mass lesions.

Methods VS and SS recordings were obtained from 28 patients with vocal fold mass lesions before and after phonomicrosurgery and 17 controls who were vocally healthy. Two clinicians rated clinically relevant vocal fold vibratory features using both imaging techniques, indicated their internal level of confidence in the accuracy of their ratings, and provided reasons for low or no confidence.

Results SS had fewer asynchronous image sequences than VS. Vibratory outcomes were able to be computed for more patients using SS. In addition, raters demonstrated better interrater reliability and reported equal or higher levels of confidence using SS than VS.

Conclusion Stroboscopic techniques on the basis of extracting the phase directly from the HSV image sequence are more reliable than acoustic-based VS. Findings suggest that SS derived from high-speed videoendoscopy is a promising improvement over current VS systems.

M. Borsky, D. D. Mehta, J. P. Gudjohnsen, and J. Gudnason, “Classification of voice modality using electroglottogram waveforms,” in INTERSPEECH, 2016.Abstract


It has been proven that the improper function of the vocal folds can result in perceptually distorted speech that is typically identified with various speech pathologies or even some neurological diseases. As a consequence, researchers have focused on finding quantitative voice characteristics to objectively assess and automatically detect non-modal voice types. The bulk of the research has focused on classifying the speech modality by using the features extracted from the speech signal. This paper proposes a different approach that focuses on analyzing the signal characteristics of the electroglottogram (EGG) waveform. The core idea is that modal and different kinds of non-modal voice types produce EGG signals that have distinct spectral/cepstral characteristics. As a consequence, they can be distinguished from each other by using standard cepstral-based features and a simple multivariate Gaussian mixture model. The practical usability of this approach has been verified in the task of classifying among modal, breathy, rough, pressed and soft voice types. We have achieved 83% frame-level accuracy and 91% utterance-level accuracy by training a speaker-dependent system.


M. Maffei, J. H. Van Stan, R. E. Hillman, and D. D. Mehta, “Correlating ambulatory voice measures with vocal fatigue self-ratings in individuals with MTD and normal controls,” Proceedings of the Annual Convention of the American Speech-Language-Hearing Association, 2016. Poster
C. E. Stepp, M. Zañartu, D. D. Mehta, and R. E. Hillman, “Hyperfunctional voice disorders: Current results, clinical implications, and future directions of a multidisciplinary research program,” Proceedings of the Annual Convention of the American Speech-Language-Hearing Association, 2016.
V. McKenna, A. Llico, D. Mehta, and C. Stepp, “Neck-surface acceleration as an estimate of subglottal pressure during modulated vocal effort in healthy speakers,” Proceedings of the Annual Convention of the American Speech-Language-Hearing Association. 2016.
D. D. Mehta, H. A. Cheyne II, A. Wehner, J. T. Heaton, and R. E. Hillman, “Accuracy of self-reported estimates of daily voice use in adults with normal and disordered voices,” American Journal of Speech-Language Pathology, vol. 25, no. 4, pp. 576-589, 2016. Paper
M. Brockmann-Bauser, J. E. Bohlender, and D. D. Mehta, “Acoustic perturbation measures improve with increasing vocal intensity in healthy and pathological voices,” Proceedings of the Voice Foundation Symposium, 2016.
N. Iftimia, G. Maguluri, E. Chang, J. Park, J. Kobler, and D. Mehta, “Dynamic vocal fold imaging with combined optical coherence tomography/high-speed video endoscopy,” Proceedings of the 10th International Conference on Voice Physiology and Biomechanics, pp. 1-2, 2016. Paper
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.

O. Murton, et al., “Impact of congestive heart failure on voice and speech production: A pilot study,” Proceedings of the Annual Scientific Meeting of the Heart Failure Society of America, 2016. Poster
R. E. Hillman, D. Mehta, C. Stepp, J. Van Stan, and M. Zanartu, “Objective assessment of vocal hyperfunction,” Proceedings of The Journal of the Acoustical Society of America, vol. 139, pp. 2193-2194, 2016.
R. L. Horwitz-Martin, et al., “Relation of automatically extracted formant trajectories with intelligibility loss and speaking rate decline in amyotrophic lateral sclerosis,” Proceedings of InterSpeech, pp. 1205-1209, 2016. Paper
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.

M. Ghassemi, Z. Syed, D. D. Mehta, J. H. Van Stan, R. E. Hillman, and J. V. Guttag, “Uncovering voice misuse using symbolic mismatch,” JMLR (Journal of Machine Learning Research): Workshop and Conference Proceedings, pp. 1-14, 2016. Paper