# Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis

arXiv: https://arxiv.org/abs/2506.12073 · HTML: https://mlsanity.com/paper/2506.12073/

## 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 (most cited first, 23 of 23)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [SeamlessM4T: Massively Multilingual & Multimodal Machine Translation](https://mlsanity.com/paper/2308.11596/) (2023)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Phone-to-audio alignment without text: A Semi-supervised Approach](https://mlsanity.com/paper/2110.03876/) (2021)
- [Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection](https://mlsanity.com/paper/2312.12810/) (2023)
- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection](https://mlsanity.com/paper/2408.15297/) (2024)
- [Towards Hierarchical Spoken Language Dysfluency Modeling](https://mlsanity.com/paper/2401.10015/) (2024)
- [Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment](https://mlsanity.com/paper/2502.07029/) (2025)
- [Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks](https://mlsanity.com/paper/2210.07499/) (2022)
- [SSDM: Scalable Speech Dysfluency Modeling](https://mlsanity.com/paper/2408.16221/) (2024)
- [Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition](https://mlsanity.com/paper/2204.00465/) (2022)
- [Deep Speech Synthesis from MRI-Based Articulatory Representations](https://mlsanity.com/paper/2307.02471/) (2023)
- [Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization](https://mlsanity.com/paper/2210.16498/) (2022)
- [Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection](https://mlsanity.com/paper/2409.09621/) (2024)
- [Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection](https://mlsanity.com/paper/2505.22029/) (2025)
- [Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling](https://mlsanity.com/paper/2306.00996/) (2023)
- [Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection](https://mlsanity.com/paper/2409.13582/) (2024)
- [Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detection](https://mlsanity.com/paper/2505.16351/) (2025)
- [SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies](https://mlsanity.com/paper/2412.00265/) (2024)

## Built on by (most cited first, 4 of 4)

- [K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function](https://mlsanity.com/paper/2507.03043/) (2025)
- [Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applications](https://mlsanity.com/paper/2508.16681/) (2025)
- [A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models](https://mlsanity.com/paper/2509.00058/) (2025)
- [Deploying UDM Series in Real-Life Stuttered Speech Applications: A Clinical Evaluation Framework](https://mlsanity.com/paper/2509.14304/) (2025)

## 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.