mlsanityData to 14 Oct 2026

Adapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German Speech

arXiv 2506.21622 ↗First posted 23 Jun 2025cs.CL cs.AI cs.SDcited by 4

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Abstract (arXiv)

Speech impairments caused by conditions such as cerebral palsy or genetic disorders pose significant challenges for automatic speech recognition (ASR) systems. Despite recent advances, ASR models like Whisper struggle with non-normative speech due to limited training data and the difficulty of collecting and annotating non-normative speech samples. In this work, we propose a practical and lightweight pipeline to personalize ASR models, formalizing the selection of words and enriching a small, speech-impaired dataset with semantic coherence. Applied to data from a child with a structural speech impairment, our approach shows promising improvements in transcription quality, demonstrating the potential to reduce communication barriers for individuals with atypical speech patterns.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 23 Jun 2025.

Builds on

10 of 27 references.

References · 10

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2020Universal Phone Recognition with a Multilingual Allophone Systemcited by 81
  3. 2019Personalizing ASR for Dysarthric and Accented Speech with Limited Datacited by 45
  4. 2022Speaker adaptation for Wav2vec2 based dysarthric ASRcited by 21
  5. 2024Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesiscited by 14
  6. 2022On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approachescited by 13
  7. 2023Use of Speech Impairment Severity for Dysarthric Speech Recognitioncited by 8
  8. 2023Accurate synthesis of Dysarthric Speech for ASR data augmentationcited by 7
  9. 2023A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessmentcited by 3
  10. 2024An Attention Long Short-Term Memory based system for automatic classification of speech intelligibilitycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Variational Low-Rank Adaptation for Personalized Impaired Speech Recognitioncited by 4
  2. 2025Data-Efficient ASR Personalization for Non-Normative Speech Using an Uncertainty-Based Phoneme Difficulty Score for Guided Samplingcited by 1
  3. 2026When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognitioncited by 1
  4. 2026Demonstration of Adapt4Me: An Uncertainty-Aware Authoring Environment for Personalizing Automatic Speech Recognition to Non-normative Speechcited by 0