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Tianjin University, Huiyan Technology (Tianjin) Co., Ltd
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Achieving 90% accuracy in Alzheimer's detection through a novel graph-based approach that captures the intricacies of spontaneous speech could revolutionize diagnostic practices.
Continuous-target modeling reveals a shared semantic mapping for ASR and S2TT, challenging conventional views on their error sources.
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.
