Cross-domain Fisher criterion for remote therapeutic efficacy assessment of Parkinson's disease via speech recognition.
Cross-domain Fisher criterion for remote therapeutic efficacy assessment of Parkinson's disease via speech recognition.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Chongqing, CN · Author affiliation
School of Computer Science and Engineering, Chongqing University of Science and Technology, Chongqing, China.Location evidence
Sydney, AU · Author affiliation
School of Computer Science, The University of Sydney, Sydney, NSW, Australia.Location evidence
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Original abstract
To address the limitation that prevailing cross-domain Parkinson's disease (PD) speech recognition methods mitigate small-sample issues via distribution matching while disregarding substantial feature overlap, we introduce the cross-domain Fisher criterion (CFC). CFC reformulates inter- and intra-class scatter matrices to align with classifier properties: the former enhances separation among heterogeneous target-domain samples, whereas the latter compactly aggregates homologous cross-domain samples around the target centroid and suppresses inter-domain disparities. Numerous experimental results demonstrate that CFC is an effective, efficient, and robust method for PD speech recognition, offering a promising approach for integration into PD speech-based remote rehabilitation and monitoring systems..