Dysarthria severity assessment using squeeze-and-excitation networks

Automated dysarthria severity identification can aid clinicians in monitoring the patient's progress, and can improve the performance of dysarthric speech recognition systems. In this paper, we explore the potency of squeeze-and-excitation (SE) networks for dysarthria severity level classification using mel spectrograms. Deep convolutional neural network (CNN) models built using SE components and residual blocks are analysed in this regard. A comparison of the system performance is done against a shallow CNN and a convolutional recurrent neural network built using a bidirectional long short-term memory network. The models are evaluated on the Universal Access dysarthric speech corpus under speaker-dependent (SD) and speaker-independent (SID) scenarios. The proposed model obtained appreciable results in the SD scenario, and its margin of about 10% over the baselines in the SID scenario promises its applicability in automated dysarthria severity level classification.