Improving dysarthric speech classification with generative AI-based data augmentation

Dysarthria is a neurological speech disorder characterized by impaired motor control of speech production, resulting in reduced intelligibility. It commonly arises in the context of neurodegenerative conditions, including amyotrophic lateral sclerosis and cerebral palsy. The development of deep learning systems for dysarthria assessment is hindered by the scarcity of annotated data, which restricts the ability of models to generalize to unseen speakers. This study proposes a cross-corpus diffusion-based data augmentation strategy for improving automatic speech intelligibility classification from dysarthric speech. A Grad-TTS model is trained on dysarthric speakers from the TORGO dataset and used to synthesize the vocabulary of UA-Speech, producing speaker-conditioned samples intended to transfer pathological acoustic traits across corpora. The proposed strategy is evaluated both independently and in combination with conventional signal-level augmentation using ResNet-50 and ResNet-152 classifiers in binary and four-class intelligibility classification tasks. The results indicate that cross-corpus synthetic augmentation improves average performance and that, in most settings, its integration with conventional augmentation yields further gains, suggesting that the two strategies introduce complementary forms of variability.