Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices

The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies. Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, aged 18–80 years) performing three speech tasks: (a) sustained vowel, (b) maximum phonation, and (c) the second and third sentences of the Rainbow passage. Data were analyzed to extract mean fundamental frequency (fo), maximum phonation time (MPT), and cepstral peak prominence (CPP) using Praat and our custom smartphone algorithms. Linear regression models were calculated with and without outliers to determine relationships. Statistically significant relationships were found between the smartphone algorithms and Praat for all three measures (r2 = 0.68–0.95, with outliers; r2 = 0.80–0.98, without outliers). An offset between CPP measures was found where Praat values were consistently lower than those computed by the smartphone app. Outlying data were identified and described, and findings indicated that speakers with high levels of clinician-perceived dysphonia resulted in smartphone algorithm errors. These results suggest that the proposed algorithms can provide measurements comparable to clinically derived values. However, clinicians should take caution when analyzing severely dysphonic voices as the current algorithms show reduced accuracy for measures of mean fo and MPT for these voice types.