Validation of an Algorithm for Semi-automated Estimation of Voice Relative Fundamental Frequency

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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.

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