mlsanityData to 19 Oct 2026

ASR Personalization in Pathological Voice: Accuracy, Efficiency, and Duration Effects

arXiv 2610.24572 ↗First posted 17 Oct 2026cs.SD cs.AI cs.CLcited by 0

On the map: Speech and audio language models → ASR evaluation, bias and atypical speech

Abstract (arXiv)

Automatic Speech recognition (ASR) struggles with Pathological speech due to persistent speaker-specific mismatch. In clinical and accessibility scenarios, only limited target-speaker data is typically available, making full model retraining impractical. We present a component-level analysis of LoRA adaptation in Whisper, investigating how adapter placement interacts with the model size, training efficiency and utterance-duration in single-speaker settings. Our experimental comparative study shows that while scaling reduces errors, it does not eliminate mismatch. Furthermore, the full attention LoRa provides the most reliable accuracy, and the best time--accuracy trade-off. Improvements vary across utterance length, with medium-length recordings benefiting the most, while short and long utterances remain challenging. These findings highlight the importance of adaptation placement and duration-dependent evaluation, under challenging, data-scarce personalization scenarios.

Key facts. No citing papers in the map yet (data through 19 Oct 2026). Placed in ASR evaluation, bias and atypical speech, one of 11 directions in Speech and audio language models. Builds on 12 of its 30 references that are in the map. First posted on arXiv 17 Oct 2026.

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12 of 30 references.

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Same direction, newest first

  1. 2024Hypernetworks for Personalizing ASR to Atypical Speech6 Jun

Other references · 11

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,833
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,661
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 4,385
  4. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 2,293
  5. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,270
  6. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 899
  7. 2020MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformerscited by 247
  8. 2023Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggerscited by 84
  9. 2019Personalizing ASR for Dysarthric and Accented Speech with Limited Datacited by 58
  10. 2024LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASRcited by 47
  11. 2021Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speechcited by 39

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  1. 2026Understanding Personalization Gains in Whisper
  2. 2026Adapting Foundation ASR Models to Dysarthric Speech: A Case Study
  3. 2025Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition
  4. 2025Data-Efficient ASR Personalization for Non-Normative Speech Using an Uncertainty-Based Phoneme Difficulty Score for Guided Sampling
  5. 2026Choosing a PEFT Variant for Per-Patient Dysarthric ASR: A Single-Speaker Case Study on Two ASR Bases
  6. 2025Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation
  7. 2021Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets
  8. 2023Adapting an Unadaptable ASR System