Speech Intelligibility Classifiers from 550k Disordered Speech Samples

no code implementations • 13 Mar 2023 • 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

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.

Assessing ASR Model Quality on Disordered Speech using BERTScore

no code implementations • 21 Sep 2022 • Jimmy Tobin, Qisheng Li, Subhashini Venugopalan, Katie Seaver, Richard Cave, Katrin Tomanek

BERTScore was found to be more correlated with human assessment of error type and assessment.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets

no code implementations • 9 Oct 2021 • Jimmy Tobin, Katrin Tomanek

Word error rate (WER) thresholds were selected to determine Success Percentage (the percentage of personalized models reaching the target WER) in different application scenarios.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases

no code implementations • 8 Jul 2021 • Subhashini Venugopalan, Joel Shor, Manoj Plakal, Jimmy Tobin, Katrin Tomanek, Jordan R. Green, Michael P. Brenner

Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment.

Task 2