Probability-Based Best Sample Selection for Acoustic Analysis of Normal and Disordered Voices

Abstract

Acoustic analysis is a commonly used method for quantitatively measuring vocal fold function. The accuracy of acoustic analysis depends upon the operator selecting a stable segment of the voice sample to analyze. This paper proposes a novel method to more accurately and reliably select a stable voice segment. Four selection methods were implemented to evaluate each raw audio signal and determine the most stable segment of each signal: The proposed modal periodogram method, the moving window method, the midvowel method, and the whole vowel method. Acoustic parameters of interest-namely perturbation (jitter), correlation dimension (D2), and spectrum convergence ratio (SCR)-were calculated for 48 phonation samples to evaluate each method. The proposed modal periodogram method utilizes a minimum mean-square error based approach to calculate a stable modal periodogram and obtain the most stable segment. The Wilcoxon Signed-Rank test was used to compare jitter, D2, and SCR values acquired using the modal periodogram method against the current standard segment selection methods. The modal periodogram method yielded significantly lower D2 values, and a significantly higher SCR for both normal and disordered voice samples (P < 0.01). This indicates that the modal periodogram method is more apt for selecting a stable audio segment than the other selection methods.