mlsanityData to 13 Oct 2026

StutterNet: Stuttering Detection Using Time Delay Neural Network

arXiv 2105.05599 ↗First posted 12 May 2021eess.AS cs.LG cs.SDcited by 20

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Abstract (arXiv)

This paper introduces StutterNet, a novel deep learning based stuttering detection capable of detecting and identifying various types of disfluencies. Most of the existing work in this domain uses automatic speech recognition (ASR) combined with language models for stuttering detection. Compared to the existing work, which depends on the ASR module, our method relies solely on the acoustic signal. We use a time-delay neural network (TDNN) suitable for capturing contextual aspects of the disfluent utterances. We evaluate our system on the UCLASS stuttering dataset consisting of more than 100 speakers. Our method achieves promising results and outperforms the state-of-the-art residual neural network based method. The number of trainable parameters of the proposed method is also substantially less due to the parameter sharing scheme of TDNN.

Key facts. Cited by 20 papers in the map (data through 13 Oct 2026). Builds on 3 of its 27 references that are in the map. First posted on arXiv 12 May 2021.

Builds on

3 of 27 references.

References · 3

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,223
  2. 2020Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trendscited by 28
  3. 2019Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memorycited by 27

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 4; 2024: 3; 2025: 2; 2026: 4.

20 papers.

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  1. 2022Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0cited by 26
  2. 2021Machine Learning for Stuttering Identification: Review, Challenges and Future Directionscited by 18
  3. 2022Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Passcited by 11
  4. 2022Filler Word Detection and Classification: A Dataset and Benchmarkcited by 7
  5. 2022Robust Stuttering Detection via Multi-task and Adversarial Learningcited by 6
  6. 2022Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detectioncited by 5
  7. 2022End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challengecited by 5
  8. 2023Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learningcited by 5
  9. 2023Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimizationcited by 5
  10. 2023Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddingscited by 4
  11. 2024Self-supervised Speech Models for Word-Level Stuttered Speech Detectioncited by 3
  12. 2025StutterZero and StutterFormer: End-to-End Speech Conversion for Stuttering Transcription and Correctioncited by 1
8 more
  1. 2023ASTER: Automatic Speech Recognition System Accessibility Testing for Stuttererscited by 0
  2. 2024Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's Encoder for Efficient Parameter Reduction in Automated Assessmentcited by 0
  3. 2024MMSD-Net: Towards Multi-modal Stuttering Detectioncited by 0
  4. 2025StutterFuse: Mitigating Modality Collapse in Stuttering Detection with Jaccard-Weighted Metric Learning and Gated Fusioncited by 0
  5. 2026Multilingual Stutter Event Detection for English, German, and Mandarin Speechcited by 0
  6. 2026Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapycited by 0
  7. 2026Cripping AI: Reimagining AI Through Lived Disability Experiencescited by 0
  8. 2026Paediatric-HGNN: A Hybrid Heterogeneous Graph Neural Network for Detecting Disfluency in Children's Speech via Multiscale Acoustic Fusioncited by 0