  This paper presents our approach for the SemEval-2024 Task 10: Emotion
Discovery and Reasoning its Flip in Conversations. For the Emotion Recognition
in Conversations (ERC) task, we utilize a masked-memory network along with
speaker participation. We propose a transformer-based speaker-centric model for
the Emotion Flip Reasoning (EFR) task. We also introduce Probable Trigger Zone,
a region of the conversation that is more likely to contain the utterances
causing the emotion to flip. For sub-task 3, the proposed approach achieves a
5.9 (F1 score) improvement over the task baseline. The ablation study results
highlight the significance of various design choices in the proposed method.
