1 citations · 1 across the 5 of their papers we have counts for
5 papers
A Cookbook for Community-driven Data Collection of Impaired Speech in LowResource Languages
Sumaya Ahmed Salihs, Isaac Wiafe, Jamal-Deen Abdulai +7
This study presents an approach for collecting speech samples to build Automatic Speech Recognition (ASR) models for impaired speech, particularly, low-resource languages. It aims…
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…
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…