Enhancing Robustness in Speech-Based Parkinson's Disease Detection Via Knowledge Distillation

To comprehensively capture the diversity of articulatory impairments, multi-type speech data, such as sustained vowels, repeated syllables and text reading, should be used together for Parkinson's Disease (PD) detection. However, in real applications, it is common that one or more types of speech data are unavailable or incomplete, which severely reduces the robustness and generalization ability of the PD detection model. To alleviate this issue, in this paper, a PD detection framework based on knowledge distillation is proposed. The proposed framework consists of a teacher network and a student network. The teacher network is trained on speech data with complete types, and provides multi-level supervision information to the student network. The types of speech data for student network are incomplete or missing. The teacher and student network all include three modules, i.e., feature extraction module, gated mechanism module, and multi-scale representation module. Especially, the gated mechanism module adopts an attentionbased gated neural fusion mechanism to adaptively adjust the contribution weights of each type of speech data. Moreover, hierarchical mutual information maximization mechanism, central moment discrepancy loss and output-level distillation constraints are employed to ensure robust PD detection performance even in the presence of missing data types. The experimental results on Chinese and Italian PD speech datasets show that the proposed framework exhibits strong robustness and high accuracy under different data types missing scenarios.