AI RESEARCH
TAAC: A gate into Trustable Audio Affective Computing
arXiv CS.AI
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ArXi:2603.25570v1 Announce Type: cross With the emergence of AI techniques for depression diagnosis, the conflict between high demand and limited supply for depression screening has been significantly alleviated. Among various modal data, audio-based depression diagnosis has received increasing attention from both academia and industry since audio is the most common carrier of emotion transmission. Unfortunately, audio data also contains User-sensitive Identity Information (ID), which is extremely vulnerable and may be maliciously used during the smart diagnosis process.