CROSS-MODAL AI ARCHITECTURES FOR AUDIO-VISUAL EMOTION RECOGNITION

Emotion recognition technology has become a critical area in affective computing, aiming to enablemachines to understand human affect through various signals. While unimodal systems based on either audio orvisual data provide partial insights into emotions, they fall short in capturing the multimodal nature of humanemotional expression. Cross-modal Artificial Intelligence (AI) architectures integrate both audio and visual inputsto provide a more robust and holistic emotion recognition capability. This paper explores the foundationaltechnologies of cross-modal emotion recognition, including deep learning, feature fusion, and multimodalrepresentation learning. Practical use cases in virtual assistants, mental health diagnostics, education, andcustomer service are discussed, alongside real-world applications. Ethical concerns regarding privacy,surveillance, and emotional manipulation are examined, as well as limitations such as model interpretability,cultural variability, and data alignment challenges. Finally, the paper highlights future prospects includingadaptive emotion-aware systems, neurosymbolic AI, and the growing importance of context in emotionalmodeling. Cross-modal AI stands as a powerful tool for creating emotionally intelligent systems capable ofunderstanding and interacting with humans in deeper, more meaningful ways.