# Analyzing Speech Condition Effects in Dysarthric ASR: A Layer-wise Probing Study

arXiv: https://arxiv.org/abs/2608.01865 · HTML: https://mlsanity.com/paper/2608.01865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis](https://mlsanity.com/paper/2406.08568/) (2024)
- [UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization](https://mlsanity.com/paper/2401.14664/) (2024)
- [CDSD: Chinese Dysarthria Speech Database](https://mlsanity.com/paper/2310.15930/) (2023)
- [On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2203.14593/) (2022)
- [DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model](https://mlsanity.com/paper/2506.00350/) (2025)
- [Probing Whisper for Dysarthric Speech in Detection and Assessment](https://mlsanity.com/paper/2510.04219/) (2025)
- [CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstruction](https://mlsanity.com/paper/2406.08336/) (2024)
- [Enhancing Voice Wake-Up for Dysarthria: Mandarin Dysarthria Speech Corpus Release and Customized System Design](https://mlsanity.com/paper/2406.10304/) (2024)
- [Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation](https://mlsanity.com/paper/2407.18461/) (2024)
- [A Self-Training Approach for Whisper to Enhance Long Dysarthric Speech Recognition](https://mlsanity.com/paper/2506.22810/) (2025)

## Abstract (arXiv)

Automatic speech recognition (ASR) performance degrades sharply on dysarthric speech, yet how disordered articulation reshapes a model's internal representations is underexplored. We conduct a layer-wise probing analysis of a transformer ASR encoder on Mandarin dysarthric speech under three transcript-matched conditions: original dysarthric speech, speaker conditioned zero-shot TTS resynthesis, and unconditioned TTS. Probing reveals a task- and condition-dependent representation hierarchy: phoneme boundary information remains weak across all layers for dysarthric speech; phoneme identity is recoverable in deep layers for synthetic speech, but remains poor for dysarthric speech; and recognition difficulty is concentrated in the deepest layers. Furthermore, lexical tone is a persistent error source across all conditions. Guided by these insights, layer-selective LoRA shows that mid-layer adaptation (layer 7 or layers 5-8) recovers near-full encoder performance on dysarthric speech within 6.67% and 2.89% relative margins while training only 0.16% and 0.65% of adapter parameters. Conversely, upper-layer adaptation benefits synthetic speech more than dysarthric speech. These findings link representation analysis to parameter-efficient fine-tuning and motivate layer-aware adaptation for low-resource Mandarin dysarthric ASR.