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

arXiv: https://arxiv.org/abs/1910.12590 · HTML: https://mlsanity.com/paper/1910.12590/

## Key facts

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

## Built on by (most cited first, 27 of 27)

- [Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0](https://mlsanity.com/paper/2204.03417/) (2022)
- [KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering](https://mlsanity.com/paper/2203.05383/) (2022)
- [StutterNet: Stuttering Detection Using Time Delay Neural Network](https://mlsanity.com/paper/2105.05599/) (2021)
- [Machine Learning for Stuttering Identification: Review, Challenges and Future Directions](https://mlsanity.com/paper/2107.04057/) (2021)
- [SSDM: Scalable Speech Dysfluency Modeling](https://mlsanity.com/paper/2408.16221/) (2024)
- [Analysis and Tuning of a Voice Assistant System for Dysfluent Speech](https://mlsanity.com/paper/2106.11759/) (2021)
- [Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass](https://mlsanity.com/paper/2202.05396/) (2022)
- [A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem](https://mlsanity.com/paper/2305.19255/) (2023)
- [Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection](https://mlsanity.com/paper/2409.09621/) (2024)
- [Robust Stuttering Detection via Multi-task and Adversarial Learning](https://mlsanity.com/paper/2204.01735/) (2022)
- [Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection](https://mlsanity.com/paper/2204.01564/) (2022)
- [End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge](https://mlsanity.com/paper/2207.10817/) (2022)
- [Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning](https://mlsanity.com/paper/2302.11343/) (2023)
- [Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization](https://mlsanity.com/paper/2311.05203/) (2023)
- [Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection](https://mlsanity.com/paper/2409.13582/) (2024)
- [Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings](https://mlsanity.com/paper/2306.00689/) (2023)
- [Missingness-resilient Video-enhanced Multimodal Disfluency Detection](https://mlsanity.com/paper/2406.06964/) (2024)
- [Audio Representation Learning by Distilling Video as Privileged Information](https://mlsanity.com/paper/2302.02845/) (2023)
- [Boli: A dataset for understanding stuttering experience and analyzing stuttered speech](https://mlsanity.com/paper/2501.15877/) (2025)
- [MMSD-Net: Towards Multi-modal Stuttering Detection](https://mlsanity.com/paper/2407.11492/) (2024)
- [Stuttering-Aware Automatic Speech Recognition for Indonesian Language](https://mlsanity.com/paper/2601.03727/) (2026)
- [Multilingual Stutter Event Detection for English, German, and Mandarin Speech](https://mlsanity.com/paper/2603.26939/) (2026)
- [Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy](https://mlsanity.com/paper/2605.01101/) (2026)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)
- [A Semi-Supervised Framework for Speech Confidence Detection using Whisper](https://mlsanity.com/paper/2605.12387/) (2026)
- [Paediatric-HGNN: A Hybrid Heterogeneous Graph Neural Network for Detecting Disfluency in Children's Speech via Multiscale Acoustic Fusion](https://mlsanity.com/paper/2606.08210/) (2026)
- [Semi-Supervised Speech Confidence Detection using Pseudo-Labelling and Whisper Embeddings](https://mlsanity.com/paper/2606.16505/) (2026)

## 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%