TAAC: A gate into Trustable Audio Affective Computing

1. 1Develop audio-based depression diagnosis models using AI techniques
2. 2Implement measures to protect User-sensitive Identity Information (ID) in audio data
3. 3Evaluate the performance of TAAC in real-world scenarios
4. 4Refine the model to improve accuracy and trustability

AI engineers and researchers on a team can benefit from TAAC to develop more accurate depression diagnosis models, while data scientists can apply the findings to improve audio-based affective computing

💡 TAAC can alleviate the conflict between high demand and limited supply for depression screening using audio-based diagnosis

💡 Trustable audio affective computing for depression diagnosis #AI #AudioAffectiveComputing