Speech Intelligibility Classifiers from 550k Disordered Speech Samples

Subhashini Venugopalan; Jimmy Tobin; Samuel J. Yang; Katie Seaver; Richard J. N. Cave; Pan-Pan Jiang; Neil Zeghidour; Rus Heywood; Jordan Green; Michael P. Brenner

arXiv · 2303.07533

Speech Intelligibility Classifiers from 550k Disordered Speech Samples

Subhashini Venugopalan·Jimmy Tobin·Samuel J. Yang·Katie Seaver·Richard J. N. Cave·Pan-Pan Jiang·Neil Zeghidour·Rus Heywood·Jordan Green·Michael P. Brenner

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Abstract

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).

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Subhashini Venugopalan, Jimmy Tobin, Samuel J. Yang, Katie Seaver, Richard J. N. Cave, Pan-Pan Jiang, Neil Zeghidour, Rus Heywood, Jordan Green, Michael P. Brenner. 2023-03-13. Speech Intelligibility Classifiers from 550k Disordered Speech Samples. https://arxiv.org/abs/2303.07533

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