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Cited by 4Stuttering Research and TreatmentSpeech Recognition and SynthesisVoice and Speech Disorders

Our Collective Voices: The Social and Technical Values of a Grassroots Chinese Stuttered Speech Dataset

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Li, Jingjin, Li, Qisheng, Gong, Rong, Wang, Lezhi, Wu, Shaomei·2025·Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3715275.3732179
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Abstract

The lack of authentic stuttered speech data has significantly limited the development of stuttering friendly automatic speech recognition (ASR) models.In previous work, we collaborated with Stam-merTalk, a grassroots community of Chinese-speaking people who stutter (PWS), to collect the first stuttered speech dataset in Mandarin Chinese, containing 50 hours of conversational and commandrecitation speech from 72 PWS.This work examines both the technical and social dimensions of the dataset.Through quantitative and qualitative analysis, as well as benchmarking and fine-tuning ASR models using the dataset, we demonstrate its technical value in capturing stuttered speech at an unprecedented scale and diversity -enabling better understanding and mitigation of fluency bias in ASR -and its social value in promoting self-advocacy and structural change for PWS in China.By foregrounding lived experiences of PWS in their own voices, we also see the potential of this dataset to normalize speech disfluencies and cultivate deeper empathy for stuttering within the AI research community.

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Cited by

Sorted by citations

Authors
Li, J., Li, Q., Gong, R., Wang, L., & Wu, S.
Affiliations
  • AImpower.org, Santa Clara, California, USA
  • AImpower.org, Seattle, Washington, USA
  • StammerTalk, Vienna, Austria
  • StammerTalk, Austin, Texas, USA
  • AImpower.org, Mountain View, California, USA
ORCID
Venue
Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2025-06-23
Pages
2768-2783
DOI
10.1145/3715275.3732179
OpenAlex
W4411550047
Cited by
4
References
11
Citations by year
2026: 4
FWCI
47.36
Type
conference paper
Language
en
Open access
Licence
Locations
ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
Funders
NSF (National Science Foundation) (2427710)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Stuttering Research and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Health Sciences › Medicine › Physiology › Voice and Speech Disorders
Keywords
stuttered speech, stuttering, Mandarin Chinese, speech dataset, speech recognition, grassroots communities, self-advocacy, disfluency, conversational speech

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