mlsanityData to 14 Oct 2026

Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis

arXiv 2506.12073 ↗First posted 5 Jun 2025eess.AS cs.AI cs.CLcited by 4

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Abstract (arXiv)

Accurate alignment of dysfluent speech with intended text is crucial for automating the diagnosis of neurodegenerative speech disorders. Traditional methods often fail to model phoneme similarities effectively, limiting their performance. In this work, we propose Neural LCS, a novel approach for dysfluent text-text and speech-text alignment. Neural LCS addresses key challenges, including partial alignment and context-aware similarity mapping, by leveraging robust phoneme-level modeling. We evaluate our method on a large-scale simulated dataset, generated using advanced data simulation techniques, and real PPA data. Neural LCS significantly outperforms state-of-the-art models in both alignment accuracy and dysfluent speech segmentation. Our results demonstrate the potential of Neural LCS to enhance automated systems for diagnosing and analyzing speech disorders, offering a more accurate and linguistically grounded solution for dysfluent speech alignment.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 5 Jun 2025.

Builds on

23 of 39 references.

References · 23

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
  3. 2023Scaling Speech Technology to 1,000+ Languagescited by 411
  4. 2023SeamlessM4T: Massively Multilingual & Multimodal Machine Translationcited by 125
  5. 2020Universal Phone Recognition with a Multilingual Allophone Systemcited by 81
  6. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  7. 2021Phone-to-audio alignment without text: A Semi-supervised Approachcited by 26
  8. 2023Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detectioncited by 21
  9. 2024Coding Speech through Vocal Tract Kinematicscited by 18
  10. 2024YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detectioncited by 16
  11. 2024Towards Hierarchical Spoken Language Dysfluency Modelingcited by 15
  12. 2025Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessmentcited by 15
11 more
  1. 2022Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence taskscited by 13
  2. 2024SSDM: Scalable Speech Dysfluency Modelingcited by 13
  3. 2022Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decompositioncited by 12
  4. 2023Deep Speech Synthesis from MRI-Based Articulatory Representationscited by 12
  5. 2022Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorizationcited by 11
  6. 2024Stutter-Solver: End-to-end Multi-lingual Dysfluency Detectioncited by 8
  7. 2025Analysis and Evaluation of Synthetic Data Generation in Speech 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. 2025Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detectioncited by 5
  11. 2024SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluenciescited by 4

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Functioncited by 1
  2. 2025Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applicationscited by 0
  3. 2025A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Modelscited by 0
  4. 2025Deploying UDM Series in Real-Life Stuttered Speech Applications: A Clinical Evaluation Frameworkcited by 0