# Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection

arXiv: https://arxiv.org/abs/2312.12810 · HTML: https://mlsanity.com/paper/2312.12810/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2023.

## Built on by (most cited first, 21 of 21)

- [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)
- [SSDM: Scalable Speech Dysfluency Modeling](https://mlsanity.com/paper/2408.16221/) (2024)
- [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)
- [Augmenting Automatic Speech Recognition Models with Disfluency Detection](https://mlsanity.com/paper/2409.10177/) (2024)
- [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)
- [Self-supervised Speech Models for Word-Level Stuttered Speech Detection](https://mlsanity.com/paper/2409.10704/) (2024)
- [Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis](https://mlsanity.com/paper/2506.12073/) (2025)
- [Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing](https://mlsanity.com/paper/2607.18934/) (2026)
- [Smooth Operators: LLMs Translating Imperfect Hints into Disfluency-Rich Transcripts](https://mlsanity.com/paper/2506.18510/) (2025)
- [K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function](https://mlsanity.com/paper/2507.03043/) (2025)
- [Enabling Conversational Behavior Reasoning Capabilities in Full-Duplex Speech](https://mlsanity.com/paper/2512.21706/) (2025)
- [Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech](https://mlsanity.com/paper/2407.03132/) (2024)
- [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)
- [On the Difficulty of Token-Level Modeling of Dysfluency and Fluency Shaping Artifacts](https://mlsanity.com/paper/2512.02027/) (2025)
- [HASS: Hierarchical Simulation of Logopenic Aphasic Speech for Scalable PPA Detection](https://mlsanity.com/paper/2603.26795/) (2026)
- [What Counts as an Error? Dual-Reference Benchmarking for Atypical ASR](https://mlsanity.com/paper/2606.31112/) (2026)

## Abstract (arXiv)

Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primarily focuses on either transcription or detection, and the performance of each aspect remains limited. In this work, we present an unconstrained dysfluency modeling (UDM) approach that addresses both transcription and detection in an automatic and hierarchical manner. UDM eliminates the need for extensive manual annotation by providing a comprehensive solution. Furthermore, we introduce a simulated dysfluent dataset called VCTK++ to enhance the capabilities of UDM in phonetic transcription. Our experimental results demonstrate the effectiveness and robustness of our proposed methods in both transcription and detection tasks.