mlsanityData to 14 Oct 2026

Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memory

arXiv 1910.12590 ↗First posted 17 Oct 2019eess.AS cs.LG cs.SDcited by 27

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Abstract (arXiv)

Stuttering is a speech impediment affecting tens of millions of people on an everyday basis. Even with its commonality, there is minimal data and research on the identification and classification of stuttered speech. This paper tackles the problem of detection and classification of different forms of stutter. As opposed to most existing works that identify stutters with language models, our work proposes a model that relies solely on acoustic features, allowing for identification of several variations of stutter disfluencies without the need for speech recognition. Our model uses a deep residual network and bidirectional long short-term memory layers to classify different types of stutters and achieves an average miss rate of 10.03%, outperforming the state-of-the-art by almost 27%

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2019.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 5; 2024: 5; 2025: 1; 2026: 7.

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  1. 2022Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0cited by 27
  2. 2022KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stutteringcited by 21
  3. 2021StutterNet: Stuttering Detection Using Time Delay Neural Networkcited by 20
  4. 2021Machine Learning for Stuttering Identification: Review, Challenges and Future Directionscited by 18
  5. 2024SSDM: Scalable Speech Dysfluency Modelingcited by 13
  6. 2021Analysis and Tuning of a Voice Assistant System for Dysfluent Speechcited by 12
  7. 2022Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Passcited by 11
  8. 2023A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problemcited by 9
  9. 2024Stutter-Solver: End-to-end Multi-lingual Dysfluency Detectioncited by 8
  10. 2022Robust Stuttering Detection via Multi-task and Adversarial Learningcited by 6
  11. 2022Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detectioncited by 5
  12. 2022End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challengecited by 5
15 more
  1. 2023Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learningcited by 5
  2. 2023Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimizationcited by 5
  3. 2024Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detectioncited by 5
  4. 2023Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddingscited by 4
  5. 2024Missingness-resilient Video-enhanced Multimodal Disfluency Detectioncited by 4
  6. 2023Audio Representation Learning by Distilling Video as Privileged Informationcited by 3
  7. 2025Boli: A dataset for understanding stuttering experience and analyzing stuttered speechcited by 3
  8. 2024MMSD-Net: Towards Multi-modal Stuttering Detectioncited by 0
  9. 2026Stuttering-Aware Automatic Speech Recognition for Indonesian Languagecited by 0
  10. 2026Multilingual Stutter Event Detection for English, German, and Mandarin Speechcited by 0
  11. 2026Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapycited by 0
  12. 2026Cripping AI: Reimagining AI Through Lived Disability Experiencescited by 0
  13. 2026A Semi-Supervised Framework for Speech Confidence Detection using Whispercited by 0
  14. 2026Paediatric-HGNN: A Hybrid Heterogeneous Graph Neural Network for Detecting Disfluency in Children's Speech via Multiscale Acoustic Fusioncited by 0
  15. 2026Semi-Supervised Speech Confidence Detection using Pseudo-Labelling and Whisper Embeddingscited by 0