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Tianjin University
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Leading models fall short in emotional intelligence, with EmoS achieving 83.8% accuracy and nearing human-level performance.
Achieving 90% accuracy in Alzheimer's detection through a novel graph-based approach that captures the intricacies of spontaneous speech could revolutionize diagnostic practices.
Iteratively prompting a graph neural network at test time to amplify out-of-distribution signals dramatically improves OOD detection accuracy.
Overcome Alzheimer's speech detection's data scarcity with FAL-AD, a federated learning framework that hits 91.52% accuracy by generating synthetic speech samples and aligning acoustic and textual features.
