2 citations · 2 across the 7 of their papers we have counts for
7 papers
The Interspeech 2025 Speech Accessibility Project Challenge
Xiuwen Zheng, Bornali Phukon, Jonghwan Na +13
While the last decade has witnessed significant advancements in Automatic Speech Recognition (ASR) systems, performance of these systems for individuals with speech disabilities re…
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…
Speech Recognition With LLMs Adapted to Disordered Speech Using Reinforcement Learning
Chirag Nagpal, Subhashini Venugopalan, Jimmy Tobin +3
We introduce a large language model (LLM) capable of processing speech inputs and show that tuning it further with reinforcement learning on human preference (RLHF) enables it to a…
Towards a Single ASR Model That Generalizes to Disordered Speech
Jimmy Tobin, Katrin Tomanek, Subhashini Venugopalan
This study investigates the impact of integrating a dataset of disordered speech recordings (1,000 hours) into the fine-tuning of a near state-of-the-art ASR baseline system.…
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…
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion
Katrin Tomanek, Shanqing Cai, Subhashini Venugopalan
Abbreviation expansion is a strategy used to speed up communication by limiting the amount of typing and using a language model to suggest expansions. Here we look at personalizing…
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…