2 citations · 3 across the 5 of their papers we have counts for
5 papers
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech
Pan-Pan Jiang, Jimmy Tobin, Katrin Tomanek +6
Project Euphonia, a Google initiative, is dedicated to improving automatic speech recognition (ASR) of disordered speech. A central objective of the project is to create a large, h…
Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments
Shanqing Cai, Subhashini Venugopalan, Katie Seaver +13
Finding ways to accelerate text input for individuals with profound motor impairments has been a long-standing area of research. Closing the speed gap for augmentative and alternat…
Speech Intelligibility Classifiers from 550k Disordered Speech Samples
Subhashini Venugopalan, Jimmy Tobin, Samuel J. Yang +7
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…
An analysis of degenerating speech due to progressive dysarthria on ASR performance
Katrin Tomanek, Katie Seaver, Pan-Pan Jiang +3
Although personalized automatic speech recognition (ASR) models have recently been designed to recognize even severely impaired speech, model performance may degrade over time for…
Assessing ASR Model Quality on Disordered Speech using BERTScore
Jimmy Tobin, Qisheng Li, Subhashini Venugopalan +3
Word Error Rate (WER) is the primary metric used to assess automatic speech recognition (ASR) model quality. It has been shown that ASR models tend to have much higher WER on speak…