Emotion recognition in conversation (ERC) requires understanding both contextual dependencies and speaker-specific cues. Existing approaches often treat conversation context as a single representation or encode speaker identity shallowly, limiting their ability to capture fine-grained emotional dynamics. We propose PERC, a personality-aware ERC framework that (1) segregates conversational context into intra- and inter-speaker components, (2) models static or dynamic personality traits to represent stable and evolving speaker dispositions, and (3) performs contrastive cross-alignment between intra–intra and inter–inter representations to enforce contextual and personality consistency. Experiments on three ERC benchmarks show that PERC achieves new state-of-the-art performance, improving weighted F1 by up to 2.74% over non-LLM methods and 0.98% over recent LLM-based methods. Our results demonstrate the effectiveness of integrating context segregation, personality modeling, and contrastive alignment for emotion reasoning in dialogue.