Predicting disordered speech comprehensibility from Goodness of Pronunciation scores

Abstract : Speech production assessment in disordered speech relies on tests such as intelligibility and/or comprehensibility tests. These tests are subjective and time-consuming for both the patients and the practitioners. In this paper, we report on the use of automatically-derived pronunciation scores to predict comprehensibility ratings, on a pilot development corpus comprised of 120 utterances recorded by 12 speakers with distinct pathologies. We found high correlation values (0.81) between Goodness Of Pronunciation (GOP) scores and comprehensibility ratings. We compare the use of a baseline implementation of the GOP algorithmwith a variant called forced-GOP, which showed better results. A linear regression model allowed to predict comprehensibility scores with a 20.9% relative error, compared to the reference scores given by two expert judges. A correlation value of 0.74 was obtained between both the manual and the predicted scores. Most of the prediction errors concern the speakers who have the most extreme ratings (the lowest or the largest values), showing that the predicted score range was globally more limited than the one of the manual scores due to the simplicity of the model.
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Lionel Fontan, Thomas Pellegrini, Júlia Olcoz, Alberto Abad. Predicting disordered speech comprehensibility from Goodness of Pronunciation scores. Workshop on Speech and Language Processing for Assistive Technologies (SLPAT 2015) satellite workshop of Interspeech 2015, Sep 2015, Dresden, Germany. pp. 1-5. ⟨hal-01316846⟩

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