# A Self-Training Approach for Whisper to Enhance Long Dysarthric Speech Recognition

arXiv: https://arxiv.org/abs/2506.22810 · HTML: https://mlsanity.com/paper/2506.22810/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Huntington Disease Automatic Speech Recognition with Biomarker Supervision](https://mlsanity.com/paper/2603.11168/) (2026)
- [Analyzing Speech Condition Effects in Dysarthric ASR: A Layer-wise Probing Study](https://mlsanity.com/paper/2608.01865/) (2026)

## Abstract (arXiv)

Dysarthric speech recognition (DSR) enhances the accessibility of smart devices for dysarthric speakers with limited mobility. Previously, DSR research was constrained by the fact that existing datasets typically consisted of isolated words, command phrases, and a limited number of sentences spoken by a few individuals. This constrained research to command-interaction systems and speaker adaptation. The Speech Accessibility Project (SAP) changed this by releasing a large and diverse English dysarthric dataset, leading to the SAP Challenge to build speaker- and text-independent DSR systems. We enhanced the Whisper model's performance on long dysarthric speech via a novel self-training method. This method increased training data and adapted the model to handle potentially incomplete speech segments encountered during inference. Our system achieved second place in both Word Error Rate and Semantic Score in the SAP Challenge.