Choosing a PEFT Variant for Per-Patient Dysarthric ASR: A Single-Speaker Case Study on Two ASR Bases

arXiv:2609.02735 · cs.CL, cs.SD · Submitted 2026-09-02 · Read on arXiv

cs.CL, cs.SD

Submitted: 2026-09-02

Updated: 2026-09-02

Comments: 2 figures. Submitted to Speech Communication (Elsevier)

Code: https://github.com/huggingface/peft

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 41× in this paper · explained in 5 episodes
• Adaptation — 31× in this paper · explained in 1 episode
• Transformer — 9× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 4× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Automatic Speech Recognition (ASR) — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 2× in this paper
• Personalization — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation
• Personalized Fine-Tuning with Controllable Synthetic Speech from LLM-Generated Transcripts for Dysarthric Speech Recognition
• AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
• FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning
• QLoRA: Efficient Finetuning of Quantized LLMs
• DoRA: Weight-Decomposed Low-Rank Adaptation
• VeRA: Vector-based Random Matrix Adaptation
• VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
• Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition
• Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
• Improved Dysarthric Speech to Text Conversion via TTS Personalization
• Personalizing ASR for Dysarthric and Accented Speech with Limited Data
• Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets
• Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech
• Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition
• Adapting Foundation ASR Models to Dysarthric Speech: A Case Study
• FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition
• FiLM: Visual Reasoning with a General Conditioning Layer
• Robust Speech Recognition via Large-Scale Weak Supervision
• Qwen3-ASR Technical Report

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering