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

arXiv: https://arxiv.org/abs/2506.21622 · HTML: https://mlsanity.com/paper/2506.21622/

## 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 (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [Speaker adaptation for Wav2vec2 based dysarthric ASR](https://mlsanity.com/paper/2204.00770/) (2022)
- [Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis](https://mlsanity.com/paper/2406.08568/) (2024)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [Use of Speech Impairment Severity for Dysarthric Speech Recognition](https://mlsanity.com/paper/2305.10659/) (2023)
- [Accurate synthesis of Dysarthric Speech for ASR data augmentation](https://mlsanity.com/paper/2308.08438/) (2023)
- [A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment](https://mlsanity.com/paper/2306.04337/) (2023)
- [An Attention Long Short-Term Memory based system for automatic classification of speech intelligibility](https://mlsanity.com/paper/2402.02850/) (2024)

## Built on by (most cited first, 4 of 4)

- [Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition](https://mlsanity.com/paper/2509.20397/) (2025)
- [Data-Efficient ASR Personalization for Non-Normative Speech Using an Uncertainty-Based Phoneme Difficulty Score for Guided Sampling](https://mlsanity.com/paper/2509.20396/) (2025)
- [When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition](https://mlsanity.com/paper/2605.02782/) (2026)
- [Demonstration of Adapt4Me: An Uncertainty-Aware Authoring Environment for Personalizing Automatic Speech Recognition to Non-normative Speech](https://mlsanity.com/paper/2603.20112/) (2026)

## 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.