Emotion Recognition in Conversational AI Using Context-Aware Transformer Networks
Keywords:
Emotion recognition; conversational AI; context-aware transformer networks; affective computing; system governance; deployment sustainabilityAbstract
Emotion recognition in conversational AI has moved from isolated utterance classification to context-sensitive modeling of multi-turn dialogue. Context-aware transformer networks provide a flexible architectural basis for capturing long-range dependencies, speaker dynamics, and evolving emotional states. This paper examines these systems from a systems research perspective, considering not only predictive performance but also structural trade-offs, data infrastructure, deployment constraints, fairness, governance, and sustainability. The discussion begins with the evolution from recurrent and graph-based dialogue models to pretrained transformer encoders, then analyzes how context can be encoded through speaker segments, turn-level positional signals, memory mechanisms, and attention regularization. It further addresses annotation quality, dataset biases, label subjectivity, and evaluation protocol limitations. Rather than proposing a single optimal architecture, the paper argues that emotion recognition in conversational AI should be treated as a socio-technical infrastructure problem in which model architecture is coupled with data governance, monitoring, and organizational accountability. The analysis includes considerations of latency, memory, energy consumption, fine-tuning efficiency, and cross-domain generalization. It also explores regulatory and ethical implications when emotion inference is deployed in education, healthcare, customer service, and public-sector settings. The paper concludes that context-aware transformer networks are promising but require careful system design and institutional safeguards to ensure robust, fair, and sustainable deployment.
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