Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluation

Speech recognition models, predominantly trained on standard speech, often exhibit lower accuracy for individuals with accents, dialects, or speech impairments. This disparity is particularly pronounced for economically or socially marginalized communities, including those with disabilities or diver...

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Main Authors: Alicia Martin, Robert L. MacDonald, Pan-Pan Jiang, Marilyn Ladewig, Julie Cattiau, Rus Heywood, Richard Cave, Jimmy Tobin, Philip C. Nelson, Katrin Tomanek
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Language Sciences
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Online Access:https://www.frontiersin.org/articles/10.3389/flang.2025.1569448/full
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author Alicia Martin
Robert L. MacDonald
Pan-Pan Jiang
Marilyn Ladewig
Julie Cattiau
Rus Heywood
Richard Cave
Jimmy Tobin
Philip C. Nelson
Katrin Tomanek
author_facet Alicia Martin
Robert L. MacDonald
Pan-Pan Jiang
Marilyn Ladewig
Julie Cattiau
Rus Heywood
Richard Cave
Jimmy Tobin
Philip C. Nelson
Katrin Tomanek
author_sort Alicia Martin
collection DOAJ
description Speech recognition models, predominantly trained on standard speech, often exhibit lower accuracy for individuals with accents, dialects, or speech impairments. This disparity is particularly pronounced for economically or socially marginalized communities, including those with disabilities or diverse linguistic backgrounds. Project Euphonia, a Google initiative originally launched in English dedicated to improving Automatic Speech Recognition (ASR) of disordered speech, is expanding its data collection and evaluation efforts to include international languages like Spanish, Japanese, French and Hindi, in a continued effort to enhance inclusivity. This paper presents an overview of the extension of processes and methods used for English data collection to more languages and locales, progress on the collected data, and details about our model evaluation process, focusing on meaning preservation based on Generative AI.
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series Frontiers in Language Sciences
spelling doaj-art-97199d5cd20d4962a99e4a2ff7614f0c2025-08-20T03:21:39ZengFrontiers Media S.A.Frontiers in Language Sciences2813-46052025-06-01410.3389/flang.2025.15694481569448Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluationAlicia Martin0Robert L. MacDonald1Pan-Pan Jiang2Marilyn Ladewig3Julie Cattiau4Rus Heywood5Richard Cave6Jimmy Tobin7Philip C. Nelson8Katrin Tomanek9Google Research, Mountain View, CA, United StatesGoogle Research, Mountain View, CA, United StatesGoogle Research, Mountain View, CA, United StatesCP Unlimited, New York, NY, United StatesGoogle Research, Mountain View, CA, United StatesGoogle Research, Mountain View, CA, United StatesComputer Science Department, University College London (UCL), London, United KingdomGoogle Research, Mountain View, CA, United StatesGoogle Research, Mountain View, CA, United StatesGoogle Research, Mountain View, CA, United StatesSpeech recognition models, predominantly trained on standard speech, often exhibit lower accuracy for individuals with accents, dialects, or speech impairments. This disparity is particularly pronounced for economically or socially marginalized communities, including those with disabilities or diverse linguistic backgrounds. Project Euphonia, a Google initiative originally launched in English dedicated to improving Automatic Speech Recognition (ASR) of disordered speech, is expanding its data collection and evaluation efforts to include international languages like Spanish, Japanese, French and Hindi, in a continued effort to enhance inclusivity. This paper presents an overview of the extension of processes and methods used for English data collection to more languages and locales, progress on the collected data, and details about our model evaluation process, focusing on meaning preservation based on Generative AI.https://www.frontiersin.org/articles/10.3389/flang.2025.1569448/fulldisordered speechautomatic speech recognitionspeech data collectiondysarthriaartificial intelligence
spellingShingle Alicia Martin
Robert L. MacDonald
Pan-Pan Jiang
Marilyn Ladewig
Julie Cattiau
Rus Heywood
Richard Cave
Jimmy Tobin
Philip C. Nelson
Katrin Tomanek
Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluation
Frontiers in Language Sciences
disordered speech
automatic speech recognition
speech data collection
dysarthria
artificial intelligence
title Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluation
title_full Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluation
title_fullStr Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluation
title_full_unstemmed Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluation
title_short Project Euphonia: advancing inclusive speech recognition through expanded data collection and evaluation
title_sort project euphonia advancing inclusive speech recognition through expanded data collection and evaluation
topic disordered speech
automatic speech recognition
speech data collection
dysarthria
artificial intelligence
url https://www.frontiersin.org/articles/10.3389/flang.2025.1569448/full
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