[2603.25570] TAAC: A gate into Trustable Audio Affective Computing

Computer Science > Cryptography and Security arXiv:2603.25570 (cs) [Submitted on 26 Mar 2026] Title:TAAC: A gate into Trustable Audio Affective Computing Authors:Xintao Hu, Feng-Qi Cui View a PDF of the paper titled TAAC: A gate into Trustable Audio Affective Computing, by Xintao Hu and 1 other authors View PDF HTML (experimental) Abstract: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. Among previous methods, the clarification between depression features and sensitive features has always serve as a barrier. It is also critical to the problem for introducing a safe encryption methodology that only encrypts the sensitive features and a powerful classifier that can correctly diagnose the depression. To track these challenges, by leveraging adversarial loss-based Subspace Decomposition, we propose a first practical framework \name presented for Trustable Audio Affective Computing, to perform automated depression detection through audio within a trustable environment. The key enablers of TAAC ar...