This Phase I SBIR will develop a new clinical software that can be used by Speech Language Pathologists (SLPs) to inform the assessment and treatment of Muscle Tension Dysphonia (MTD), one of the most common voice disorders in clinical practice. MTD is characterized by abnormal coactivation of muscles in and around the larynx that causes vocal strain - leading to discomfort and pain that impede vocal communication and may lead to surgery. While voice therapy can mitigate these health risks by reducing laryngeal tension and restoring muscle balance, the current standard for guiding therapy is by manual palpations and auditory-perceptual impressions – highly subjective methods with poor inter-rater reliability. The most direct approach to quantitative measures of laryngeal muscle tension is manual analysis of vocal fold images from an endoscopy – an invasive and time- consuming method that is not clinically viable for routine assessment and management of MTD. These methods are also incompatible with the recent need for technology that facilitates remote assessment in the wake of COVID-19. We will address these needs by developing a new non-contact software tool that provides accurate, valid, acoustic measures of MTD during speech exercises involving the offset and onset of vocalization. This approach has been shown in our prior NIH-funded studies to elicit measurable acoustic changes associated with the presence and absence of MTD and following voice therapy. Leveraging this clinical foundation, our team of signal processing experts at Altec Inc. will partner with speech researchers, SLPs and otolaryngologists at Boston University to translate our preliminary laboratory-based software into a turn-key clinical tool to support both in-person and remote assessment and management of MTD. We will do so by translating our existing time- consuming manual approach for measuring laryngeal tension into clinical software that uses fully-automated algorithms to achieve high accuracy when tested on an existing database of N=450 individuals with and without MTD (Aim 1). The automated algorithms will be complemented by software to guide patient compliance with the MTD-specific speech exercises and provide SLPs with quantitative outcome measures for a robust, clinically- viable implementation (Aim 2). Aim 3 will evaluate our MTD software prototype by N=4 SLPs in N=12 patients with MTD to demonstrate the proof of concept that: 1) it provides accurate measures compared to validated manual procedures; 2) it detects changes in laryngeal tension due to increased vocal effort or laryngeal massage therapy with >95% agreement to manual procedures; and 3) it achieves favorable ratings of feasibility, acceptability and perceived value by patients and SLPs. Phase II will advance our Phase I prototype with real- time calculations, an acoustic signal quality monitor, and a HIPPA-compliant data management infrastructure for clinical point-of-care or remote use. It’s reliance on low-cost, readily available, non-contact sensing provides a practical and effective tool to support in-person and remote clinical care for those with MTD that advances the NIDCD priorities for technologies to improve health among individuals with speech and language disorders. 1

Nearly half of all voice disorders referred to a Speech Language Pathologist suffer from excessive laryngeal muscle tension known as Muscle Tension Dysphonia (MTD). This project develops the first non-contact solution for quantitative clinical management of MTD by developing software that automates acoustic analysis of voice samples from MTD-specific speech exercises to provide clinically validated outcome measures of laryngeal tension that can be used for assessing baseline vocal behavior and tracking progress during voice therapy. The project advances the NIDCD priorities to promote new technologies for improving health and wellness among individuals with speech and language disorders by meeting the high-impact need for objective, low-cost and readily available tools for restoring vocal function in the clinic as well as improving the quality, accessibility and affordability of remote healthcare in the wake of COVID-19.