Emotion-Aware AI Communication Interfaces: Enhancing Expressive Capabilities for Deaf, Non-Verbal, and Speech-Impaired Users

Communication is the structural base of human experience, and many people who have to meet difficult communication needs, such as deaf and non-verbal or speech-impaired people, face daunting structural challenges in reaching expressive parity.Although important, conventional Augmentative and Alternative Communication (AAC) systems are often limited due to the lack of conversational speed, contextual awareness, and, most importantly, a lack of the ability to communicate subtle emotional and affective conditions.The theoretical review fills this gap by integrating the emerging developments in the context of affective computing, assistive technology (AT), humancomputer interaction (HCI) and accessibility research.In the overall analysis, it is evident that the ongoing Artificial Intelligence (AI) applications are characterized by serious developmental challenges, especially with regard to pervasive biases of data due to homogeneous or non-performed emotional datasets, and the inability to effectively analyze non-homogeneous, non-standard expressive modalities, including non-verbal speech and dysarthric communication.Most importantly, the current models are deficient in a prescriptive framework of ethically implementing automated emotional responsiveness.As a result, this article presents an original conceptual framework titled Emotion-Aware Multimodal AAC Interaction Cycle (EA-MAAC), which is based on individual multimodal input, ethically responsible affective perception, contextual thinking, and adapting emotional response.With an exceedingly well-organized sequence of attack, the EA-MAAC model offers engineering nextgeneration interfaces that focus on user autonomy, access policy requirements, and universal principles of design, and thus improves the comprehensive expressive abilities and social assimilation of underserved populations.