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Clinical diagnosis of stuttering requires an assessment by a licensed speech-language pathologist. However, this process is time-consuming and requires clinicians with training and experience in stuttering and fluency disorders. Unfortunately, only a small percentage of speech-language pathologists report being comfortable working with individuals who stutter, which is inadequate to accommodate for the 80 million individuals who stutter worldwide. Developing machine learning models for detecting stuttered speech would enable universal and automated screening for stuttering, enabling speech pathologists to identify and follow up with patients who are most likely to be diagnosed with a stuttering speech disorder. Previous research in this area has predominantly focused on utterance-level detection, which is not sufficient for clinical settings where word-level annotation of stuttering is the norm. In this study, we curated a stuttered speech dataset with word-level annotations and introduced a word-level stuttering speech detection model leveraging self-supervised speech models. Our evaluation demonstrates that our model surpasses previous approaches in word-level stuttering speech detection. Additionally, we conducted an extensive ablation analysis of our method, providing insight into the most important aspects of adapting self-supervised speech models for stuttered speech detection.
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This content will become publicly available on June 23, 2026
Our Collective Voices: The Social and Technical Values of a Grassroots Chinese Stuttered Speech Dataset
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 StammerTalk, 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 command-recitation 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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- Award ID(s):
- 2427710
- PAR ID:
- 10618283
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400714825
- Page Range / eLocation ID:
- 2768 to 2783
- Format(s):
- Medium: X
- Location:
- Athens Greece
- Sponsoring Org:
- National Science Foundation
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