Revisiting dysarthria assessment intelligibility metrics

This study reports on the development of an automated isolated-word intelligibility metric system designed to improve the scoring consistency and reliability of the Frenchay Dysarthria Assessment Test (FDA).The proposed intelligibility measurements are based on the probabilistic likelihood scores derived from the forced alignment of the dysarthric speech to whole-word hidden Markov models (HMMs) trained on data from a variety of normal speakers. The hypothesis is that these probability scores are correlated to the decoding effort made by naïve listeners when trying to comprehend dysarthric utterances. Initial results indicate that the scores returned from these composite measurements provide a more fine-grained assessment of a given dysarthric individual’s oral communicative competence when compared with traditional ‘right-or-wrong’ scoring of expert listeners’ transcriptions of dysarthric speech samples.