Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update

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

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Contains table of contents for Part V, table of contents for Section 1, reports on six research projects and a list of publications.C.J. Lebel FellowshipDennis Klatt Memorial FundNational Institutes of Health Grant R01-DC00075National Institutes of Health Grant R01-DC01291National Institutes of Health Grant R01-DC01925National Institutes of Health Grant R01-DC02125National Institutes of Health Grant R01-DC02978National Institutes of Health Grant R01-DC03007National Institutes of Health Grant R29-DC02525National Institutes of Health Grant F32-DC00194National Institutes of Health Grant F32-DC00205National Institutes of Health Grant T32-DC00038National Science Foundation Grant IRI 89-05249National Science Foundation Grant IRI 93-14967National Science Foundation Grant INT 94-2114

Smartphone-based detection of voice disorders by long-term monitoring of neck acceleration features

Abstract—Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, termed vocal hyperfunction. Thus an ongoing goal in clinical voice assessment is the longterm monitoring of noninvasively derived measures to track hyperfunction. This paper reports on a smartphone-based voice health monitor that records the high-bandwidth accelerometer signal from the neck skin above the collarbone. Data collection is under way from patients with vocal hyperfunction and matchedcontrol subjects to create a dataset designed to identify the best set of diagnostic measures for hyperfunctional patterns of vocal behavior. Vocal status is tracked from neck acceleration using previously-developed vocal dose measures and novel model-based features of glottal airflow estimates. Clinically, the treatment of hyperfunctional disorders would be greatly enhanced by the ability to unobtrusively monitor and quantify detrimental behaviors and, ultimately, to provide real-time biofeedback that could facilitate healthier voice use. Index Terms—voice use, vocal hyperfunction, voice production model, accelerometer sensor, wearable voice sensor I

Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules

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.National Library of Medicine (U.S.). Biomedical Informatics Research Training ProgramIntel Corporation. Science and Technology CenterNational Institute on Deafness and Other Communication Disorders (U.S.) (Grant t R33 DC011588)Comision Nacional de Investigacion Ciencia y Tecnologia (Chile) (Grant FONDECYT 11110147)MIT International Science and Technology Initiatives. MIT-Chile Seed Fund (Grant 2745333

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Contains table of contents for Part V, table of contents for Section 1, reports on six research projects and a list of publications.C.J. Lebel FellowshipDennis Klatt Memorial FundNational Institutes of Health Grant F32-DC00194National Institutes of Health Grant F32-DC00205National Institutes of Health Grant P01-DC00361National Institutes of Health Grant R01-DC00075National Institutes of Health Grant R01-DC00261National Institutes of Health Grant R01-DC00266National Institutes of Health Grant R01-DC01291National Institutes of Health Grant R01-DC01925National Institutes of Health Grant R03-DC01721National Institutes of Health Grant R29 DC02525National Institutes of Health Grant T32-DC00038National Science Foundation Grant INT 94-21146National Science Foundation Grant IRI 89-0543

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Contains table of contents for Part IV, table of contents for Section 1, reports on six research projects, one report on the research laboratory and a list of publications.C.J Lebel FellowshipDennis Klatt Memorial FundNational Institutes of Health Grant R01-DC00075National Institutes of Health Grant R01-DC01291National Institutes of Health Grant R01-DC01925National Institutes of Health Grant R01-DC02125National Institutes of Health Grant R01-DC02978National Institutes of Health Grant R01-DC03007National Institutes of Health Grant R29-DC02525-01A1National Institutes of Health Grant F32-DC00194National Institutes of Health Grant F32-DC00205National Institutes of Health Grant T32-DC00038National Science Foundation Grant IRI 93-14967National Science Foundation Grant INT 94-2114