Results 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.

Many common voice disorders are chronic or recurring conditions that are likely to result from faulty and/or abusive patterns of vocal behavior, referred to generically as vocal hyperfunction. An ongoing goal in clinical voice assessment is the development and use of noninvasively derived measures to quantify and track the daily status of vocal hyperfunction so that the diagnosis and treatment of such behaviorally based voice disorders can be improved. This paper reports on the development of a new, versatile, and cost-effective clinical tool for mobile voice monitoring that acquires the high-bandwidth signal from an accelerometer sensor placed on the neck skin above the collarbone. Using a smartphone as the data acquisition platform, the prototype device provides a user-friendly interface for voice use monitoring, daily sensor calibration, and periodic alert capabilities. Pilot data are reported from three vocally normal speakers and three subjects with voice disorders to demonstrate the potential of the device to yield standard measures of fundamental frequency and sound pressure level and model-based glottal airflow properties. The smartphone-based platform enables future clinical studies for the identification of the best set of measures for differentiating between normal and hyperfunctional patterns of voice use.

1In individuals with major depressive disorder, neurophysiological changes often alter motor control and thus affect the mechanisms controlling speech production and facial expression. These changes are typically associated with psychomotor retardation, a condition marked by slowed neuromotor output that is behaviorally manifested as altered coordination and timing across multiple motor-based properties. Changes in motor outputs can be inferred from vocal acoustics and facial movements as individuals speak. We derive novel multi-scale correlation structure and timing feature sets from audio-based vocal features and videobased facial action units from recordings provided by the 4th International Audio/Video Emotion Challenge (AVEC). The feature sets enable detection of changes in coordination, movement, and timing of vocal and facial gestures that are potentially symptomatic of depression. Combining complementary features in Gaussian mixture model and extreme learning machine classifiers, our multivariate regression scheme predicts Beck depression inventory ratings on the AVEC test set with a root-mean-square error of 8.12 and mean absolute error of 6.31. Future work calls for continued study into detection of neurological disorders based on altered coordination and timing across audio and video modalities.