Subtext word accuracy and prosodic features for automatic intelligibility assessment

Abstract

Speech intelligibility for voice rehabilitation can successfully be evaluated by automatic prosodic analysis. In this paper, the influence of reading errors and the selection of certain words (nouns only, nouns and verbs, beginning of each sentence, beginnings of sentences and subclauses) for the computation of the word accuracy (WA) and prosodic features are examined. 73 hoarse patients read the German version of the text “The North Wind and the Sun”. Their intelligibility was evaluated perceptually by 5 trained experts according to a 5-point scale. Combining prosodic features and WA by Support Vector Regression showed human-machine correlations of up to r=0.86. They drop for files with few reading errors, however, but this can largely be evened out by feature set adjustment. WA should be computed on the whole text, but for some prosodic features, a subset of words may be sufficient.