# Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis

arXiv: https://arxiv.org/abs/2406.08568 · HTML: https://mlsanity.com/paper/2406.08568/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2024.

## Built on by (most cited first, 14 of 14)

- [Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap](https://mlsanity.com/paper/2410.16726/) (2024)
- [Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR](https://mlsanity.com/paper/2501.10256/) (2025)
- [Adapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German Speech](https://mlsanity.com/paper/2506.21622/) (2025)
- [Exploring Generative Error Correction for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.20163/) (2025)
- [Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation](https://mlsanity.com/paper/2505.20606/) (2025)
- [Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech](https://mlsanity.com/paper/2506.01618/) (2025)
- [Data-Efficient ASR Personalization for Non-Normative Speech Using an Uncertainty-Based Phoneme Difficulty Score for Guided Sampling](https://mlsanity.com/paper/2509.20396/) (2025)
- [Controllable Dysarthric Speech Synthesis with Patient-Specific Conditioning for Speaker-Diverse ASR Augmentation](https://mlsanity.com/paper/2602.08696/) (2026)
- [Elderly-Contextual Data Augmentation via Speech Synthesis for Elderly ASR](https://mlsanity.com/paper/2604.24770/) (2026)
- [AP-GRPO: Anchor-Gated Phonetic Alignment with Policy Optimization for Pathological Speech Reconstruction](https://mlsanity.com/paper/2606.15540/) (2026)
- [Augmenting Dysarthric Speech Severity Assessment with MOS Supervision](https://mlsanity.com/paper/2606.18645/) (2026)
- [Low-Burden Data Augmentation for Dysarthric ASR via Zero-Shot Voice Cloning](https://mlsanity.com/paper/2606.19823/) (2026)
- [Analyzing Speech Condition Effects in Dysarthric ASR: A Layer-wise Probing Study](https://mlsanity.com/paper/2608.01865/) (2026)
- [Continuous-Time Acoustic Modelling with Neural Controlled Differential Equations](https://mlsanity.com/paper/2609.11725/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) research has achieved impressive performance in recent years and has significant potential for enabling access for people with dysarthria (PwD) in augmentative and alternative communication (AAC) and home environment systems. However, progress in dysarthric ASR (DASR) has been limited by high variability in dysarthric speech and limited public availability of dysarthric training data. This paper demonstrates that data augmentation using text-to-dysarthic-speech (TTDS) synthesis for finetuning large ASR models is effective for DASR. Specifically, diffusion-based text-to-speech (TTS) models can produce speech samples similar to dysarthric speech that can be used as additional training data for fine-tuning ASR foundation models, in this case Whisper. Results show improved synthesis metrics and ASR performance for the proposed multi-speaker diffusion-based TTDS data augmentation for ASR fine-tuning compared to current DASR baselines.