mlsanityData to 7 Oct 2026

Machine Learning for Stuttering Identification: Review, Challenges and Future Directions

arXiv 2107.04057 ↗First posted 8 Jul 2021cs.SD cs.LG eess.AScited by 18

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Abstract (arXiv)

Stuttering is a speech disorder during which the flow of speech is interrupted by involuntary pauses and repetition of sounds. Stuttering identification is an interesting interdisciplinary domain research problem which involves pathology, psychology, acoustics, and signal processing that makes it hard and complicated to detect. Recent developments in machine and deep learning have dramatically revolutionized speech domain, however minimal attention has been given to stuttering identification. This work fills the gap by trying to bring researchers together from interdisciplinary fields. In this paper, we review comprehensively acoustic features, statistical and deep learning based stuttering/disfluency classification methods. We also present several challenges and possible future directions.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 18 of its 146 references that are in the map. First posted on arXiv 8 Jul 2021.

Builds on

18 of 146 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  4. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  5. 2015Attention-Based Models for Speech Recognitioncited by 400
  6. 2020Rethinking the Value of Labels for Improving Class-Imbalanced Learningcited by 216
  7. 2021SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stuttercited by 51
  8. 2016Disfluency Detection using a Bidirectional LSTMcited by 31
  9. 2020Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trendscited by 28
  10. 2019Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memorycited by 27
  11. 2018Attention Based Fully Convolutional Network for Speech Emotion Recognitioncited by 23
  12. 2020Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detectioncited by 23
6 more
  1. 2021StutterNet: Stuttering Detection Using Time Delay Neural Networkcited by 20
  2. 2020FluentNet: End-to-End Detection of Speech Disfluency with Deep Learningcited by 9
  3. 2020Visually Guided Self Supervised Learning of Speech Representationscited by 8
  4. 2017End-to-End Attention based Text-Dependent Speaker Verificationcited by 7
  5. 2018Learning to detect dysarthria from raw speechcited by 6
  6. 2021Learnable MFCCs for Speaker Verificationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 6; 2025: 3; 2026: 1.

18 papers.

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  1. 2022Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0cited by 26
  2. 2024Large Language Models for Dysfluency Detection in Stuttered Speechcited by 8
  3. 2022Robust Stuttering Detection via Multi-task and Adversarial Learningcited by 6
  4. 2022Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detectioncited by 5
  5. 2022End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challengecited by 5
  6. 2023Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learningcited by 5
  7. 2023Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimizationcited by 5
  8. 2024Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentationcited by 5
  9. 2023Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddingscited by 4
  10. 2024Missingness-resilient Video-enhanced Multimodal Disfluency Detectioncited by 4
  11. 2024Impact of Speech Mode in Automatic Pathological Speech Detectioncited by 2
  12. 2025Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasiacited by 2
6 more
  1. 2025StutterZero and StutterFormer: End-to-End Speech Conversion for Stuttering Transcription and Correctioncited by 1
  2. 2023ASTER: Automatic Speech Recognition System Accessibility Testing for Stuttererscited by 0
  3. 2024Selfsupervised learning for pathological speech detectioncited by 0
  4. 2024Machine listening in a neonatal intensive care unitcited by 0
  5. 2025Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applicationscited by 0
  6. 2026Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelinescited by 0