Speech Intelligibility Classifiers from 550k Disordered Speech Samples

03/13/2023

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by   Subhashini Venugopalan, et al.

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We developed dysarthric speech intelligibility classifiers on 551,176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking disorders and rated for their overall intelligibility on a five-point scale. We trained three models following different deep learning approaches and evaluated them on  94K utterances from 100 speakers. We further found the models to generalize well (without further training) on the TORGO database (100 ALS-TDI PMP (0.81 AUC) datasets as well as on a dataset of realistic unprompted speech we gathered (106 dysarthric and 76 control speakers, 2300 samples).