Jimmy Tobin
Jimmy Tobin is a researcher and software engineer focusing on using ML to improve accessibility. He got his BS and MA at Stanford focused on audio, ML and perception.
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Large Language Models as a Proxy For Human Evaluation in Assessing the Comprehensibility of Disordered Speech Transcription
Katrin Tomanek
Richard Cave
Katie Seaver
Jordan Green
Rus Heywood
Proceedings of ICASSP, IEEE (2024)
Automatic Speech Recognition of Conversational Speech in Individuals with Disordered Speech
Bob MacDonald
Rus Heywood
Richard Cave
Katie Seaver
Antoine Desjardins
Jordan Green
Journal of Speech, Language, and Hearing Research (2024) (to appear)
Speech Intelligibility Classifiers from 550k Disordered Speech Samples
Katie Seaver
Richard Cave
Neil Zeghidour
Rus Heywood
Jordan Green
ICASSP, Icassp submission. 2022 (2023)
Assessing ASR Model Quality on Disordered Speech using BERTScore
Qisheng Li
Katie Seaver
Richard Jonathan Noel Cave
Katrin Tomanek
Proc. 1st Workshop on Speech for Social Good (S4SG) (2022), pp. 26-30 (to appear)
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Joel Shor
Katrin Tomanek
Jordan R. Green
Interspeech, Interspeech 2021 (2021) (to appear)
Automatic Speech Recognition of Disordered Speech: Personalized models outperforming human listeners on short phrases
Jordan R. Green
Bob MacDonald
Rus Heywood
Richard Cave
Katie Seaver
Marilyn Ladewig
Katrin Tomanek
Interspeech (2021) (to appear)
Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia
Bob MacDonald
Rus Heywood
Richard Cave
Katie Seaver
Marilyn Ladewig
Jordan R. Green
Katrin Tomanek
Interspeech (2021) (to appear)
