Language-independent automatic evaluation of intelligibility of chronically hoarse persons

Automatic intelligibility assessment using automatic speech recognition is usually language specific. In this study, a language-independent approach is proposed. It uses models that are trained with Flemish speech, and it is applied to assess chronically hoarse German speakers. The research questions are here: is it possible to construct suitable acoustic features that generalize to other languages and a speech disorder, and is the generated model for intelligibility also suitable for specific subtypes of that disorder, i.e. functional and organic dysphonia?

73 German-speaking persons with chronic hoarseness read the text 'Der Nordwind und die Sonne'. Perceptual intelligibility scores were used as ground truth during the training of an automatic model that converts speaker level acoustic measurements into intelligibility scores. Cross-validation is used to assess model performance.

The interrater agreement for all patients (n = 73) and for the functional and organic dysphonia subgroups (n = 45 and n = 24) are r = 0.82, r = 0.83 and r = 0.75, respectively. The automatic assessment based on phonologically based acoustic models revealed correlations between perceptual and automatic intelligibility ratings of r = 0.79 (all patients), r = 0.78 (functional dysphonia) and r = 0.80 (organic dysphonia).

The automatic, objective measurement of intelligibility is a valuable instrument in an evidence-based clinical practice.