# AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2506.06566 · HTML: https://mlsanity.com/paper/2506.06566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity](https://mlsanity.com/paper/2108.02297/) (2021)
- [Aphasic Speech Recognition using a Mixture of Speech Intelligibility Experts](https://mlsanity.com/paper/2008.10788/) (2020)
- [A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning](https://mlsanity.com/paper/2305.13331/) (2023)
- [A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control](https://mlsanity.com/paper/2103.13452/) (2021)
- [Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition](https://mlsanity.com/paper/2204.00448/) (2022)

## Abstract (arXiv)

This paper proposes AS-ASR, a lightweight aphasia-specific speech recognition framework based on Whisper-tiny, tailored for low-resource deployment on edge devices. Our approach introduces a hybrid training strategy that systematically combines standard and aphasic speech at varying ratios, enabling robust generalization, and a GPT-4-based reference enhancement method that refines noisy aphasic transcripts, improving supervision quality. We conduct extensive experiments across multiple data mixing configurations and evaluation settings. Results show that our fine-tuned model significantly outperforms the zero-shot baseline, reducing WER on aphasic speech by over 30% while preserving performance on standard speech. The proposed framework offers a scalable, efficient solution for real-world disordered speech recognition.