8 papers
Toward a Theory of Value in AI Alignment
Andrew Smart, Shazeda Ahmed, Jackie Kay +3
Can AI systems be aligned to human values? The popularization of large language models (LLMs) and multi-modal foundation models has seen a rise in harms spanning from toxic speech…
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…
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…
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…
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets
Jimmy Tobin, Katrin Tomanek
This study investigates the performance of personalized automatic speech recognition (ASR) for recognizing disordered speech using small amounts of per-speaker adaptation data. We…
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Subhashini Venugopalan, Joel Shor, Manoj Plakal +4
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment. Classification approaches can also help…