A Study on Model Training Strategies for Speaker-Independent and Vocabulary-Mismatched Dysarthric Speech Recognition

Automatic speech recognition (ASR) systems often struggle to recognize speech from individuals with dysarthria, a speech disorder with neuromuscular causes, with accuracy declining further for unseen speakers and content. Achieving robustness for such situations requires ASR systems to address speaker-independent and vocabulary-mismatched scenarios, minimizing user adaptation effort. This study focuses on comprehensive training strategies and methods to tackle these challenges, leveraging the transformer-based Wav2Vec2.0 model. Unlike prior research, which often focuses on limited datasets, we systematically explore training data selection strategies across diverse source types (languages, canonical vs. dysarthric, and generic vs. in-domain) in a speaker-independent setting. For the under-explored vocabulary-mismatched scenarios, we evaluate conventional methods, identify their limitations, and propose a solution that uses phonological features as intermediate representations for phone recognition to address these gaps. Experimental results demonstrate that this approach enhances recognition across dysarthric datasets in both speaker-independent and vocabulary-mismatched settings. By integrating advanced transfer learning techniques with the innovative use of phonological features, this study addresses key challenges for dysarthric speech recognition, setting a new benchmark for robustness and adaptability in the field.