# Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection

arXiv: https://arxiv.org/abs/2505.22029 · HTML: https://mlsanity.com/paper/2505.22029/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 28 May 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 25 of 25)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS](https://mlsanity.com/paper/2406.18009/) (2024)
- [Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound](https://mlsanity.com/paper/2502.05139/) (2025)
- [SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter](https://mlsanity.com/paper/2102.12394/) (2021)
- [CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions](https://mlsanity.com/paper/2408.16589/) (2024)
- [Disfluency Detection using a Bidirectional LSTM](https://mlsanity.com/paper/1604.03209/) (2016)
- [Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0](https://mlsanity.com/paper/2204.03417/) (2022)
- [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)
- [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)
- [Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass](https://mlsanity.com/paper/2202.05396/) (2022)
- [Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization](https://mlsanity.com/paper/2210.16498/) (2022)
- [Large Language Models for Dysfluency Detection in Stuttered Speech](https://mlsanity.com/paper/2406.11025/) (2024)
- [Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection](https://mlsanity.com/paper/2409.09621/) (2024)
- [Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection](https://mlsanity.com/paper/2409.13582/) (2024)
- [Self-supervised Speech Models for Word-Level Stuttered Speech Detection](https://mlsanity.com/paper/2409.10704/) (2024)
- [SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies](https://mlsanity.com/paper/2412.00265/) (2024)

## Built on by (most cited first, 8 of 8)

- [Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis](https://mlsanity.com/paper/2506.12073/) (2025)
- [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)
- [Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelines](https://mlsanity.com/paper/2604.20535/) (2026)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)
- [What Counts as an Error? Dual-Reference Benchmarking for Atypical ASR](https://mlsanity.com/paper/2606.31112/) (2026)

## Abstract (arXiv)

Speech dysfluency detection is crucial for clinical diagnosis and language assessment, but existing methods are limited by the scarcity of high-quality annotated data. Although recent advances in TTS model have enabled synthetic dysfluency generation, existing synthetic datasets suffer from unnatural prosody and limited contextual diversity. To address these limitations, we propose LLM-Dys -- the most comprehensive dysfluent speech corpus with LLM-enhanced dysfluency simulation. This dataset captures 11 dysfluency categories spanning both word and phoneme levels. Building upon this resource, we improve an end-to-end dysfluency detection framework. Experimental validation demonstrates state-of-the-art performance. All data, models, and code are open-sourced at https://github.com/Berkeley-Speech-Group/LLM-Dys.