Validation of an Algorithm for Semi-automated Estimation of Voice Relative Fundamental Frequency.
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.
['Lien|Yu-An S|YS|', 'Heller Murray|Elizabeth S|ES|', 'Calabrese|Carolyn R|CR|', 'Michener|Carolyn M|CM|', 'Van Stan|Jarrad H|JH|', 'Mehta|Daryush D|DD|', 'Hillman|Robert E|RE|', 'Noordzij|J Pieter|JP|', 'Stepp|Cara E|CE|']
D000293:Adolescent D000328:Adult D000368:Aged D000369:Aged, 80 and over D000465:Algorithms D016022:Case-Control Studies D005260:Female D006801:Humans D008297:Male D008875:Middle Aged D013061:Speech Acoustics D014831:Voice D014832:Voice Disorders D055815:Young Adult
acoustics voice assessment