# Robust fine-tuning of speech recognition models via model merging: application to disordered speech

arXiv: https://arxiv.org/abs/2505.20477 · HTML: https://mlsanity.com/paper/2505.20477/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2025.

## Built on by (most cited first, 3 of 3)

- [Mind the Shift: Using Delta SSL Embeddings to Enhance Child ASR](https://mlsanity.com/paper/2601.20142/) (2026)
- [Exploring the potential and limitations of Model Merging for Multi-Domain Adaptation in ASR](https://mlsanity.com/paper/2603.05354/) (2026)
- [Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs](https://mlsanity.com/paper/2606.05846/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) has advanced with Speech Foundation Models (SFMs), yet performance degrades on dysarthric speech due to variability and limited data. This study as part of the submission to the Speech Accessibility challenge, explored model merging to improve ASR generalization using Whisper as the base SFM. We compared fine-tuning with single-trajectory merging, combining models from one fine-tuning path, and multi-run merging, merging independently trained models. Our best multi-run merging approach achieved a 12% relative decrease of WER over classic fine-tuning, and a 16.2% relative decrease on long-form audios, a major loss contributor in dysarthric ASR. Merging more and more models led to continuous gains, remained effective in low-data regimes, and generalized across model architectures. These results highlight model merging as an easily replicable adaptation method that consistently improves ASR without additional inference cost or hyperparameter tuning.