mlsanityData to 7 Oct 2026

From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

arXiv 2302.09044 ↗First posted 17 Feb 2023cs.HCcited by 18

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Abstract (arXiv)

Consumer speech recognition systems do not work as well for many people with speech diferences, such as stuttering, relative to the rest of the general population. However, what is not clear is the degree to which these systems do not work, how they can be improved, or how much people want to use them. In this paper, we frst address these questions using results from a 61-person survey from people who stutter and fnd participants want to use speech recognition but are frequently cut of, misunderstood, or speech predictions do not represent intent. In a second study, where 91 people who stutter recorded voice assistant commands and dictation, we quantify how dysfuencies impede performance in a consumer-grade speech recognition system. Through three technical investigations, we demonstrate how many common errors can be prevented, resulting in a system that cuts utterances of 79.1% less often and improves word error rate from 25.4% to 9.9%.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 4; 2026: 5.

18 papers.

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  1. 2024CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptionscited by 33
  2. 2024Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speechcited by 15
  3. 2024Large Language Models for Dysfluency Detection in Stuttered Speechcited by 8
  4. 2023Automatic Disfluency Detection from Untranscribed Speechcited by 6
  5. 2024Augmenting Automatic Speech Recognition Models with Disfluency Detectioncited by 6
  6. 2024AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detectioncited by 5
  7. 2024Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentationcited by 5
  8. 2025Boli: A dataset for understanding stuttering experience and analyzing stuttered speechcited by 3
  9. 2023Latent Phrase Matching for Dysarthric Speechcited by 2
  10. 2025Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasiacited by 2
  11. 2026Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timingcited by 2
  12. 2024Hypernetworks for Personalizing ASR to Atypical Speechcited by 1
6 more
  1. 2025Prompting Whisper for Improved Verbatim Transcription and End-to-end Miscue Detectioncited by 1
  2. 2025StutterZero and StutterFormer: End-to-End Speech Conversion for Stuttering Transcription and Correctioncited by 1
  3. 2026Unheard in the Digital Age: Rethinking AI Bias and Speech Diversitycited by 0
  4. 2026Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelinescited by 0
  5. 2026Cripping AI: Reimagining AI Through Lived Disability Experiencescited by 0
  6. 2026What Counts as an Error? Dual-Reference Benchmarking for Atypical ASRcited by 0