mlsanityData to 14 Oct 2026

Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detection

arXiv 2505.16351 ↗First posted 22 May 2025eess.AS cs.AIcited by 5

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Abstract (arXiv)

Automatic detection of speech dysfluency aids speech-language pathologists in efficient transcription of disordered speech, enhancing diagnostics and treatment planning. Traditional methods, often limited to classification, provide insufficient clinical insight, and text-independent models misclassify dysfluency, especially in context-dependent cases. This work introduces Dysfluent-WFST, a zero-shot decoder that simultaneously transcribes phonemes and detects dysfluency. Unlike previous models, Dysfluent-WFST operates with upstream encoders like WavLM and requires no additional training. It achieves state-of-the-art performance in both phonetic error rate and dysfluency detection on simulated and real speech data. Our approach is lightweight, interpretable, and effective, demonstrating that explicit modeling of pronunciation behavior in decoding, rather than complex architectures, is key to improving dysfluency processing systems.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

23 of 33 references.

References · 23

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  3. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
  4. 2020Universal Phone Recognition with a Multilingual Allophone Systemcited by 81
  5. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  6. 2024From LLM to NMT: Advancing Low-Resource Machine Translation with Claudecited by 58
  7. 2016Disfluency Detection using a Bidirectional LSTMcited by 31
  8. 2022Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0cited by 27
  9. 2023Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detectioncited by 21
  10. 2024Coding Speech through Vocal Tract Kinematicscited by 18
  11. 2024YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detectioncited by 16
  12. 2024Towards Hierarchical Spoken Language Dysfluency Modelingcited by 15
11 more
  1. 2024SSDM: Scalable Speech Dysfluency Modelingcited by 13
  2. 2022Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decompositioncited by 12
  3. 2023Deep Speech Synthesis from MRI-Based Articulatory Representationscited by 12
  4. 2022Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Passcited by 11
  5. 2022Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorizationcited by 11
  6. 2024Large Language Models for Dysfluency Detection in Stuttered Speechcited by 8
  7. 2024Stutter-Solver: End-to-end Multi-lingual Dysfluency Detectioncited by 8
  8. 2023Weakly-supervised forced alignment of disfluent speech using phoneme-level modelingcited by 7
  9. 2024Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detectioncited by 5
  10. 2024Self-supervised Speech Models for Word-Level Stuttered Speech Detectioncited by 4
  11. 2024SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluenciescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysiscited by 4
  2. 2026HuPER: A Human-Inspired Framework for Phonetic Perceptioncited by 3
  3. 2025K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Functioncited by 1
  4. 2025A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Modelscited by 0
  5. 2025Deploying UDM Series in Real-Life Stuttered Speech Applications: A Clinical Evaluation Frameworkcited by 0