Abstract

Estimating the severity of dysarthria, a speech disorder from neurological conditions, is important in medicine. It helps with diagnosis, early detection, and personalized treatment. Significant progress has been made in leveraging SSL models as feature extractors for various classification tasks, demonstrating their effectiveness. Building on this, this paper examines whether using all features extracted from SSL models is necessary for optimal dysarthria severity classification from speech. We focused on layer-wise feature analysis of one base model, Wav2Vec2-base, and four large models, Wav2Vec2-large, HuBERT-large, Data2Vec-large, and WavLM-large, using a Convolutional Neural Network (CNN) as classifier with mel-frequency cepstral coefficients (MFCC) features as baseline. Experiments showed that the later transformer layers of the SSL models were more effective in the dysarthria severity classification …

Date

October 31, 2025

Authors

Paban Sapkota, Harsh Srivastava, Hemant Kumar Kathania, Shrikanth Narayanan, Sudarsana Reddy Kadiri

Journal

Speech Communication

Pages

103326

Publisher

North-Holland