Accurately detecting emotions in conversation is a necessary yet challenging
task due to the complexity of emotions and dynamics in dialogues. The emotional
state of a speaker can be influenced by many different factors, such as
interlocutor stimulus, dialogue scene, and topic. In this work, we propose a
conversational speech emotion recognition method to deal with capturing
attentive contextual dependency and speaker-sensitive interactions. First, we
use a pretrained VGGish model to extract segment-based audio representation in
individual utterances. Second, an attentive bi-directional gated recurrent unit
(GRU) models contextual-sensitive information and explores intra- and
inter-speaker dependencies jointly in a dynamic manner. The experiments
conducted on the standard conversational dataset MELD demonstrate the
effectiveness of the proposed method when compared against state-of the-art
methods.

本文针对会话中情绪识别的复杂性，提出了一种利用双向门控循环神经网络捕捉上下文相关性和说话人之间相互作用的对话情感识别方法。实验结果表明该方法比现有的最先进方法更为有效。