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

• URL: http://arxiv.org/abs/2506.21622v1
• Date: Mon, 23 Jun 2025 15:30:50 GMT
• Title: Adapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German Speech
• Authors: Niclas Pokel, Pehuén Moure, Roman Boehringer, Yingqiang Gao,
• Abstract summary: Speech impairments caused by conditions such as cerebral palsy or genetic disorders pose significant challenges for automatic speech recognition systems.<n>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.<n>Our approach shows promising improvements in transcription quality, demonstrating the potential to reduce communication barriers for individuals with atypical speech patterns.
• Score: 0.562479170374811
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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