# Zero- and One-Shot Data Augmentation for Sentence-Level Dysarthric Speech Recognition in Constrained Scenarios

arXiv: https://arxiv.org/abs/2510.16700 · HTML: https://mlsanity.com/paper/2510.16700/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in ASR evaluation, bias and atypical speech, one of 11 directions in Speech and audio language models.
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Placed in

- Speech and audio language models / [ASR evaluation, bias and atypical speech](https://mlsanity.com/speech/asr-evaluation-bias-and-atypical-speech/)

## Builds on (most cited first, 28 of 28)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [An analysis of incorporating an external language model into a sequence-to-sequence model](https://mlsanity.com/paper/1712.01996/) (2017)
- [Cold Fusion: Training Seq2Seq Models Together with Language Models](https://mlsanity.com/paper/1708.06426/) (2017)
- [Speech Recognition with Augmented Synthesized Speech](https://mlsanity.com/paper/1909.11699/) (2019)
- [Towards audio language modeling -- an overview](https://mlsanity.com/paper/2402.13236/) (2024)
- [Investigation of Data Augmentation Techniques for Disordered Speech Recognition](https://mlsanity.com/paper/2201.05562/) (2022)
- [Recent Progress in the CUHK Dysarthric Speech Recognition System](https://mlsanity.com/paper/2201.05845/) (2022)
- [Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis](https://mlsanity.com/paper/2406.08568/) (2024)
- [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022)
- [Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation](https://mlsanity.com/paper/2401.00662/) (2024)
- [EasyCall corpus: a dysarthric speech dataset](https://mlsanity.com/paper/2104.02542/) (2021)
- [Simulating dysarthric speech for training data augmentation in clinical speech applications](https://mlsanity.com/paper/1804.10325/) (2018)
- [Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition](https://mlsanity.com/paper/2201.11571/) (2022)
- [DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model](https://mlsanity.com/paper/2306.10588/) (2023)
- [Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis](https://mlsanity.com/paper/2303.14885/) (2023)
- [Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition](https://mlsanity.com/paper/2211.01646/) (2022)
- [Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation](https://mlsanity.com/paper/2407.18461/) (2024)
- [An analysis of degenerating speech due to progressive dysarthria on ASR performance](https://mlsanity.com/paper/2211.00089/) (2022)
- [CDSD: Chinese Dysarthria Speech Database](https://mlsanity.com/paper/2310.15930/) (2023)
- [Pathological voice adaptation with autoencoder-based voice conversion](https://mlsanity.com/paper/2106.08427/) (2021)
- [Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition](https://mlsanity.com/paper/2110.00797/) (2021)
- [Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test](https://mlsanity.com/paper/2305.13108/) (2023)
- [Towards Automatic Data Augmentation for Disordered Speech Recognition](https://mlsanity.com/paper/2312.08641/) (2023)

## Abstract (arXiv)

Dysarthric speech recognition (DSR) research has witnessed remarkable progress in recent years, evolving from the basic understanding of individual words to the intricate comprehension of sentence-level expressions, all driven by the pressing communication needs of individuals with dysarthria. Nevertheless, the scarcity of available data remains a substantial hurdle, posing a significant challenge to the development of effective sentence-level DSR systems. In response to this issue, dysarthric data augmentation (DDA) has emerged as a highly promising approach. Generative models are frequently employed to generate training data for automatic speech recognition tasks. However, their effectiveness hinges on the ability of the synthesized data to accurately represent the target domain. The wide-ranging variability in pronunciation among dysarthric speakers makes it extremely difficult for models trained on data from existing speakers to produce useful augmented data, especially in zero-shot or one-shot learning settings. To address this limitation, we put forward a novel text-coverage strategy specifically designed for text-matching data synthesis. This innovative strategy allows for efficient zero/one-shot DDA, leading to substantial enhancements in the performance of DSR when dealing with unseen dysarthric speakers. Such improvements are of great significance in practical applications, including dysarthria rehabilitation programs and day-to-day common-sentence communication scenarios.