Automatic intelligibility assessment using automatic speech recogni- tion is usually language-specific. In this study, a language-independent approach based on alignment-free phonological and phonemic features is proposed. It uti- lizes models that are trained with Flemish speech, and it is applied to assess dys- phonic German speakers. In order to visualize the results, two techniques were tested: a plain selection of most relevant features emerging from Ensemble Lin- ear Regression involving feature selection, and a Sammon transform of all the features to a 3-D space. The test data comprised recordings of 73 hoarse persons (48.3 � 16.8 years) who read the German version of the text "The North Wind and the Sun". The reference evaluation was obtained by five speech therapists and physicians who rated intelligibility according to a 5-point Likert scale. In the 3-D visualization, the different levels of intelligibility were clearly separated. This could be the basis for an objective support for diagnostics in voice and speech re- habilitation.
['Tino Haderlein', 'Catherine Middag', 'Andreas K. Maier', 'Jean-Pierre Martens', 'Michael Döllinger', 'Elmar Nöth']
Visualization of Intelligibility Measured by Language-Independent Features