mlsanityData to 7 Oct 2026

Robust Stuttering Detection via Multi-task and Adversarial Learning

arXiv 2204.01735 ↗First posted 4 Apr 2022eess.AS cs.LG cs.SDcited by 6

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Abstract (arXiv)

By automatic detection and identification of stuttering, speech pathologists can track the progression of disfluencies of persons who stutter (PWS). In this paper, we investigate the impact of multi-task (MTL) and adversarial learning (ADV) to learn robust stutter features. This is the first-ever preliminary study where MTL and ADV have been employed in stuttering identification (SI). We evaluate our system on the SEP-28k stuttering dataset consisting of 20 hours (approx) of data from 385 podcasts. Our methods show promising results and outperform the baseline in various disfluency classes. We achieve up to 10%, 6.78%, and 2% improvement in repetitions, blocks, and interjections respectively over the baseline.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 4 Apr 2022.

Builds on

10 of 20 references.

References · 10

  1. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  2. 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 374
  3. 2021SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stuttercited by 51
  4. 2019Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memorycited by 27
  5. 2018Speaker-Invariant Training via Adversarial Learningcited by 23
  6. 2021StutterNet: Stuttering Detection Using Time Delay Neural Networkcited by 20
  7. 2021Machine Learning for Stuttering Identification: Review, Challenges and Future Directionscited by 18
  8. 2019Speaker-invariant Affective Representation Learning via Adversarial Trainingcited by 14
  9. 2021Analysis and Tuning of a Voice Assistant System for Dysfluent Speechcited by 12
  10. 2018To Reverse the Gradient or Not: An Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognitioncited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 0; 2026: 1.

6 papers.

  1. 2023A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problemcited by 9
  2. 2022End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challengecited by 5
  3. 2023Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddingscited by 4
  4. 2023ASTER: Automatic Speech Recognition System Accessibility Testing for Stuttererscited by 0
  5. 2024Selfsupervised learning for pathological speech detectioncited by 0
  6. 2026Multilingual Stutter Event Detection for English, German, and Mandarin Speechcited by 0