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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2303.07533 (eess)
[Submitted on 13 Mar 2023 (v1), last revised 15 Mar 2023 (this version, v2)]

Title:Speech Intelligibility Classifiers from 550k Disordered Speech Samples

Authors: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).
Comments: ICASSP 2023 camera-ready
Subjects: Audio and Speech Processing (eess.AS); Sound (cs.SD)
Cite as: arXiv:2303.07533 [eess.AS]
  (or arXiv:2303.07533v2 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2303.07533

Submission history

From: Subhashini Venugopalan [view email]
[v1] Mon, 13 Mar 2023 23:38:56 UTC (387 KB)
[v2] Wed, 15 Mar 2023 22:54:23 UTC (387 KB)
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