Voice disorder discrimination using vowel acoustic measures in female speakers.

What is already known on this subject Acoustic measures hold great value in discriminating voice disorders from normal voices. However, no study has evaluated discrimination values of a combination of sustained vowel acoustic measures that quantify additive noise, signal stability, signal periodicity, spectral slope and overall voice quality in single-gender cohorts. Previous studies have not used signal typing (the classification of the acoustic signals) for time-based measures, impacting the reliability of discrimination. What this study adds to the existing knowledge This study was the first to implement signal typing to include sustained vowel samples of Types 1 and 2 signals for discrimination statistics. We showed that a combination of vocal acoustic measures using time- and spectral-based extraction from the sustained /ɑ/ vowel evaluating additive noise, signal stability, signal periodicity, spectral slope and overall voice quality resulted in good to excellent sensitivity, specificity and discrimination accuracy. As individual measures, traditional time-based measures such as HNR had rather limited discrimination values whilst spectral-based measures provided higher discrimination values. Measures that are sensitive to signal types have low discrimination ability. What are the potential or actual clinical implications of this work? The sustained vowel /ɑ/ is a relevant, universal vocal task for clinical application using acoustic measures to discriminate female speakers with and without voice disorders if signal typing is implemented. Clinical voice assessment using vowels may not be effective if relying solely on time-based measurements. Spectral-based measures perform better in voice disorder discrimination given their insensitivity to signal types. The most effective voice disorder discrimination could only be obtained using a combination of acoustic measures that quantify major phenomena in the signals of disordered voices. Using measures extracted from both programs, Praat and ADSV, is useful given that specific settings in a program may impact on discrimination accuracy.