  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% accuracy), UASpeech (0.93 correlation),
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).
