Pathology-Aware Speech Encoding and Data Augmentation for Dysarthric Speech Recognition

  • Automatic speech recognition (ASR) for pathologic speech remains a major challenge due to high variability in articulation, phonation, and prosody distortions. In this work, we propose a pathology-aware speech encoder based on BEST-RQ pre-training, which incorporates 46k hours of speech, including pathologic and atypical speech. We continue pre-training for domain adaptation and experiment with etiology-specific codebooks. We achieve a 13.2% relative word error rate (WER) improvement using the pathology-aware speech encoder with etiology-specific continued pre-training. Additionally, we examine the impact of incorporating synthetic and out-of-domain (OOD) data to further enhance ASR performance. Synthetic data reduces WER by up to 8.7%, while OOD data improves WER by 12.2%. Finally, we introduce a semantic similaritybased data augmentation technique to optimize data selection, achieving a WER improvement of up to 9.7% while minimizing the need for additional training data.

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Metadaten
Author:Ilja Baumann, Dominik Wagner, Korbinian Riedhammer, Tobias BockletORCiD
DOI:https://doi.org/10.21437/Interspeech.2025-2724
ISSN:2958-1796
Parent Title (English):Interspeech 2025
Publisher:ISCA
Place of publication:ISCA
Document Type:conference proceeding (article)
Language:English
Reviewed:Begutachtet/Reviewed
Release Date:2025/11/06
Tag:pathologic speech recognition; self-supervised learning; speech augmentation
Pagenumber:5
First Page:3289
Last Page:3293
institutes:Fakultät Informatik
Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
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