Project Summary/Abstract Approximately 30% of US adults are affected by a voice disorder during their lives, with about 25 million people experiencing a voice disorder at any given point in time, resulting in societal costs (lost work, medical expenses, etc.) estimated at $13.5 billion annually. Vocal fatigue (VF) is viewed as an etiological and/or reactive component in most common voice disorders and is also among the most common voice-related complaints of individuals who rely on their voices to make a living (e.g., teachers, singers, etc.). Previous definitions of VF have varied with a recent attempt at consensus describing VF as a multifaceted concept that involves an individual’s self- perceived symptoms (e.g., increased effort and discomfort) and/or a deterioration in vocal function associated with an individual’s attempt to meet his/her vocal demands. Prior studies have demonstrated high speaker-to- speaker variability in recovery from VF, but the factors associated with such high variability are not determined. Unfortunately, there is a paucity of objective information about the causes and impact on phonatory mechanisms of VF, limiting efforts to prevent and clinically manage this common complaint. The objectives of this project are (1) to use a multi-modal measurement approach to comprehensively, and objectively, describe the progressive impact of VF on vocal function, (2) to quantify the underlying voicing-resting behaviors that contribute to the progression of VF and its recovery, and (3) to identify the vocal function and vocal behavior parameters that could account for the observed high variability in VF recovery trajectories. The objectives of this study are pursued using a combination of an existing ambulatory voice monitoring dataset and new data. The existing dataset includes ambulatory voice recordings from 87 vocally typical individuals (for a total of 889 days) and 123 patients with vocal hyperfunction (for a total of 763 days). The new data will include a well-controlled inlab vocal loading protocol, and three days of infield monitoring using a state-of-the-art wireless monitoring system. The inlab session will include periodic multi-modal data acquisition of vocal function (high- speed videoendoscopy, aerodynamics, electroglottography, acoustics, and neck-surface accelerometry) during the progression of the loading protocol. Statistical power of machine learning is combined with two novel ambulatory measures of vocal fold dissipated energy (reflecting vocal function) and a time series voicing-resting ratio (the temporal sequencing of phonatory and resting periods reflecting vocal behavior) to quantify progression and recovery of VF in terms of a person’s prior cumulative vocal behavior and vocal function. Achieving the goals of this project will lay the groundwork for the development of new clinical tools for preventing, assessing, and alleviating VF, which will be particularly valuable for professionals in occupations requi...