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Language is the dominant channel for detecting ambivalence and hesitancy, but leveraging ASR-erased time features can significantly boost recognition accuracy.
Federated learning models can be significantly compressed and made more fair across clients by pruning different parts of the model for different clusters of users, without sacrificing accuracy.