G. Maguluri, E. Chang, N. Iftimia, D. Mehta, and J. Kobler, “Dynamic vocal fold imaging by integrating optical coherence tomography with laryngeal high-speed video endoscopy,” Proceedings of the Conference on Lasers and Electro-Optics (CLEO), pp. 1-2, 2015.Abstract

    We demonstrate three-dimensional vocal fold imaging during phonation by integrating optical coherence tomography with high-speed videoendoscopy. Results from ex vivo larynx experiments yield reconstructed vocal fold surface contours for ten phases of periodic motion.

    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.

    J. H. Van Stan, D. D. Mehta, and R. E. Hillman, “The effect of voice ambulatory biofeedback on the daily performance and retention of a modified vocal motor behavior in participants with normal voices,” Journal of Speech, Language, and Hearing Research, vol. 58, no. 3, pp. 713-721, 2015. Publisher's VersionAbstract

    Purpose Ambulatory biofeedback has potential to improve carryover of newly established vocal motor behaviors into daily life outside of the clinic and warrants systematic research that is lacking in the literature. This proof-of-concept study was designed to establish an empirical basis for future work in this area by formally assessing whether ambulatory biofeedback reduces daily vocal intensity (performance) and the extent to which this change remains after biofeedback removal (retention). Method Six participants with normal voices wore the KayPENTAX Ambulatory Phonation Monitor for 3 baseline days followed by 4 days with biofeedback provided on odd days. Results Compared to baseline days, participants exhibited a statistically significant decrease in mean vocal intensity (4.4 dB) and an increase in compliance (16.8 percentage points) when biofeedback was provided above a participant-specific intensity threshold. After biofeedback removal, mean vocal intensity and compliance reverted back to baseline levels. Conclusions These findings suggest that although current ambulatory biofeedback approaches have potential to modify a vocal motor behavior, the modified behavior may not be retained after biofeedback removal. Future work calls for the testing of more innovative ambulatory biofeedback approaches on the basis of motor control and learning theories to improve retention of a desired vocal motor behavior.

    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.

    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.