DSSCNet: A Transfer Learning Framework for Cross-Corpus Dysarthric Speech Severity Classification
For researchers and clinicians needing automated dysarthria assessment, DSSCNet improves speaker-independent classification accuracy across different corpora.
DSSCNet uses transfer learning and multi-corpus learning to improve cross-corpus dysarthric speech severity classification, achieving 75.80% accuracy on TORGO and 68.25% on UA-Speech, outperforming state-of-the-art models.
Dysarthric speech severity classification is challenging due to speaker variability, class imbalance, and limited datasets. This study introduces DSSCNet, a deep learning model that employs transfer learning and multi-corpus learning to enhance speaker-independent classification. By pre-training on one dysarthric speech corpus and fine-tuning on another, DSSCNet achieves improved feature extraction and cross-corpus generalization. Experimental results demonstrate that DSSCNet outperforms state-of-the-art models for speaker-independent severity classification, achieving 75.80\% accuracy on TORGO and 68.25\% on UA-Speech, significantly reducing misclassification errors. The findings confirm that leveraging knowledge transfer between datasets improves model robustness, making DSSCNet well-suited for automated dysarthria assessment. This research contributes to the development of more effective assistive speech technologies for individuals with speech impairments.