A Machine Learning Approach to Analysis of Daily Vocal Function and Vocal Behavior of Individuals with Phonotraumatic Vocal Hyperfunction

Sep 25, 2024
4:00pm to 5:00pm
University of Iowa, SHC 219
250 Hawkins Drive, Iowa City, IA 52242
A Machine Learning Approach to Analysis of Daily Vocal Function and Vocal Behavior of Individuals with Phonotraumatic Vocal Hyperfunction promotional image

In-person (WJSHC 210) and via Zoom (https://uiowa.zoom.us/j/92047907158?pwd=35J1P4oAYnVfabrLo7xdtBFhrX22GC.1 )

Title:

A Machine Learning Approach to Analysis of Daily Vocal Function and Vocal Behavior of Individuals with Phonotraumatic Vocal Hyperfunction

Abstract:

Voice disorders affect approximately 8% of U.S. adults at a given point in time, with a lifetime prevalence of approximately 30% for adults. Many voice disorders are considered behavioral in nature and are associated with vocal hyperfunction, which is defined as excessive perilaryngeal musculoskeletal activity during phonation. Phonotraumatic vocal hyperfunction (PVH) is associated with the etiology and pathophysiology of vocal fold nodules and polyps, and it is among the most commonly occurring types of voice disorders. Heavy voice use in the absence of sufficient voice rest/recovery, talking loudly, and the usage of an inappropriate pitch have been hypothesized as possible etiological factors for PVH. However, the possible contribution of these factors needs to be studied and quantified in daily life and during realistic communication scenarios. Ambulatory voice monitoring (AVM) combined with advanced statistical analysis tools and machine learning methods allow the study of individuals’ vocal behavior and vocal function throughout their daily lives and in naturalistic (ecologically valid) settings. During this talk, I will present several studies covering the necessity of AVM for reliable quantification of PVH behavior, consistency of manifestation of PVH across different voice measures and different modes of phonation (singing vs. speaking), appropriate quantification of voicing-resting patterns, and phenotyping of patients with PVH.

Bio:

Dr. Hamzeh Ghasemzadeh is a Harvard Medical School-Massachusetts General Hospital Research Fellow. He got his dual PhD in “Communicative Sciences and Disorders” and “Computational Mathematics Science and Engineering” from Michigan State University. His main area of research is instrumental assessment of voice and speech. Dr. Ghasemzadeh was the recipient of the 2019 Sataloff Award for Young Investigators, co-sponsored by Elsevier and The Voice Foundation, and the CAPCSD 2020 PhD scholarship recipient. He is the PI of a recently awarded K99-R00 grant, a co-investigator of a recently renewed P50, and an active consultant on two R01 grants.

Individuals with disabilities are encouraged to attend all University of Iowa–sponsored events. If you are a person with a disability who requires a reasonable accommodation in order to participate in this program, please contact Eric Hunter in advance at (269)888-5624 or eric-hunter@uiowa.edu