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Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelines

arXiv 2604.20535 ↗First posted 22 Apr 2026cs.CL cs.HCcited by 0

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Abstract (arXiv)

Atypical speech is receiving greater attention in speech technology research, but much of this work unfolds with limited interdisciplinary dialogue. For stuttered speech in particular, it is widely recognised that current speech recognition systems fall short in practice, and current evaluation methods and research priorities are not systematically grounded in end-user experiences and needs. In this work, we analyse these gaps through 1) a scoping review of papers that deal with stuttered speech and 2) a survey of 70 stakeholders, including adults who stutter and speech-language pathologists. By analysing these two perspectives, we propose a taxonomy of stuttered-speech research, identify where current research directions diverge from the needs articulated by stakeholders, and conclude by outlining concrete guidelines and directions towards addressing the real needs of the stuttering community.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 22 Apr 2026.

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16 of 41 references.

References · 16

  1. 2019Fluent Translations from Disfluent Speech in End-to-End Speech Translationcited by 22
  2. 2022KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stutteringcited by 21
  3. 2021Machine Learning for Stuttering Identification: Review, Challenges and Future Directionscited by 18
  4. 2023From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognitioncited by 18
  5. 2024YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detectioncited by 16
  6. 2024Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speechcited by 15
  7. 2021Analysis and Tuning of a Voice Assistant System for Dysfluent Speechcited by 12
  8. 2024Stutter-Solver: End-to-end Multi-lingual Dysfluency Detectioncited by 8
  9. 2025Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detectioncited by 8
  10. 2018Towards Fluent Translations from Disfluent Speechcited by 7
  11. 2020Improved Robustness to Disfluencies in RNN-Transducer Based Speech Recognitioncited by 5
  12. 2024Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentationcited by 5
4 more
  1. 2023Adversarial Training For Low-Resource Disfluency Correctioncited by 3
  2. 2025Boli: A dataset for understanding stuttering experience and analyzing stuttered speechcited by 3
  3. 2025Clinical Annotations for Automatic Stuttering Severity Assessmentcited by 3
  4. 2023DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languagescited by 1

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