Yu-An S Lien1, Elizabeth S Heller Murray2, Carolyn R Calabrese2, Carolyn M Michener2, Jarrad H Van Stan3,4, Daryush D Mehta3,4,5, Robert E Hillman3,4,5, J Pieter Noordzij6, Cara E Stepp1,2,6. 1. 1 Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. 2. 2 Department of Speech, Language, and Hearing Sciences, Boston University, Boston, Massachusetts, USA. 3. 3 Massachusetts General Hospital Institute of Health Professions, Boston, Massachusetts, USA. 4. 4 Center for Laryngeal Surgery & Voice Rehabilitation, Massachusetts General Hospital, Boston, MA, USA. 5. 5 Department of Surgery, Harvard Medical School, Cambridge, MA, USA. 6. 6 Department of Otolaryngology - Head and Neck Surgery, Boston University School of Medicine, Boston, Massachusetts, USA.
Abstract
OBJECTIVES: Relative fundamental frequency (RFF) has shown promise as an acoustic measure of voice, but the subjective and time-consuming nature of its manual estimation has made clinical translation infeasible. Here, a faster, more objective algorithm for RFF estimation is evaluated in a large and diverse sample of individuals with and without voice disorders. METHODS: Acoustic recordings were collected from 154 individuals with voice disorders and 36 age- and sex-matched controls with typical voices. These recordings were split into training and 2 testing sets. Using an algorithm tuned to the training set, semi-automated RFF estimates in the testing sets were compared to manual RFF estimates derived from 3 trained technicians. RESULTS: The semi-automated RFF estimations were highly correlated ( r = 0.82-0.91) with the manual RFF estimates. CONCLUSIONS: Fast and more objective estimation of RFF makes large-scale RFF analysis feasible. This algorithm allows for future work to optimize RFF measures and expand their potential for clinical voice assessment.
OBJECTIVES: Relative fundamental frequency (RFF) has shown promise as an acoustic measure of voice, but the subjective and time-consuming nature of its manual estimation has made clinical translation infeasible. Here, a faster, more objective algorithm for RFF estimation is evaluated in a large and diverse sample of individuals with and without voice disorders. METHODS: Acoustic recordings were collected from 154 individuals with voice disorders and 36 age- and sex-matched controls with typical voices. These recordings were split into training and 2 testing sets. Using an algorithm tuned to the training set, semi-automated RFF estimates in the testing sets were compared to manual RFF estimates derived from 3 trained technicians. RESULTS: The semi-automated RFF estimations were highly correlated ( r = 0.82-0.91) with the manual RFF estimates. CONCLUSIONS: Fast and more objective estimation of RFF makes large-scale RFF analysis feasible. This algorithm allows for future work to optimize RFF measures and expand their potential for clinical voice assessment.
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