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Good practice Imported

Project Euphonia — Training Speech Recognition on Atypical Speech Through Co-Designed Data Collection

United States of America · Mountain View · See the United States of America profile · See the Mountain View profile

Evidence: Observational / pre–post Top 9% 96/100 · Ask Evidence Copilot about this practice

Google Research trains speech-recognition models to understand atypical speech (ALS/MND, dysarthria, Down syndrome, stuttering) using samples donated by people with impaired speech, who are involved in prototyping and feedback through partners such as Team Gleason and ALS/MND All

1.5 million
Utterances collected (as of February 2025)
~3,000
Speakers contributing to dataset (as of February 2025)
132
Additional speakers recruited internationally (2025 expansion)
Project Euphonia — Training Speech Recognition on Atypical Speech Through Co-Designed Data Collection

Details

Maturity
Scaling
Promoter
Google Research / Google.org
Period
2019-present
Keywords
AI research, speech accessibility, assistive technology

Context

Project Euphonia is a Google Research initiative that trains automatic speech recognition (ASR) and personalised communication models on atypical speech patterns, including speech affected by ALS/motor neurone disease, dysarthria, Down syndrome and stuttering.

Objectives

Rather than building a one-size-fits-all recognizer, the project aims to build personalised communication models tailored to individual speakers by recruiting people with impaired speech — via partner organisations including Team Gleason, the ALS/MND Alliance, LSVT Global and CureDuchenne — to donate speech samples and take part throughout ideation, prototyping and feedback stages.

Activities

The project expanded internationally, recruiting 132 additional speakers across four languages (38 Spanish, 14 French, 76 Japanese, 4 Hindi) in Mexico, Colombia, Peru, France, India and Japan, to test whether the recruitment-and-feedback model transfers outside English-speaking contexts.

Results

A 2025 peer-reviewed evaluation in Frontiers in Language Sciences reports that, as of February 2025, the project had collected over 1.5 million utterances from approximately 3,000 speakers.

Conclusions

The paper is candid about limitations: recruitment produced a heavily imbalanced dataset across languages, formal evaluation was completed for only two of the four added languages (French and Spanish), the French evaluation carried single-rater bias risk, and the underlying datasets are not published, for privacy reasons, which limits independent replication. Google's own materials describe participant involvement as spanning ideation, design and prototype feedback, but the peer-reviewed account itself is more precise, describing 'strategic partnerships' that facilitated recruitment rather than a documented, formal co-design governance process.

Implementation

Indicative cost
Medium (€50k–€500k)
Time to results
Long (> 3 years) — Running continuously since 2019; a 2025 paper documents a subsequent international pilot expansion.
Staffing & skills
Google Research / Google.org, Partner organisations: Team Gleason, ALS/MND Alliance, LSVT Global, CureDuchenne, Volunteer speakers with atypical speech

Conditions for success

  • Partnerships with disability and patient organisations to reach willing speech donors
  • Privacy-preserving data collection given sensitive health-related speech data
  • International recruitment partners to test cross-language transfer

Common failure modes

  • Recruitment produced a heavily imbalanced dataset across languages
  • Formal evaluation completed for only two of the four added languages (French and Spanish)
  • French evaluation carried single-rater bias risk
  • Underlying datasets not published, for privacy reasons, limiting independent replication
  • Participant involvement described by Google as spanning ideation/design/feedback, but the peer-reviewed account describes only 'strategic partnerships' facilitating recruitment rather than a documented formal co-design governance process

Commonly funded by

Own resources / municipal budget

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Data sources

Where this practice's information was retrieved from, and when.

Attachments

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