The emotion recognition in conversation (ERC) task aims to predict the
emotion label of an utterance in a conversation. Since the dependencies between
speakers are complex and dynamic, which consist of intra- and inter-speaker
dependencies, the modeling of speaker-specific information is a vital role in
ERC. Although existing researchers have proposed various methods of speaker
interaction modeling, they cannot explore dynamic intra- and inter-speaker
dependencies jointly, leading to the insufficient comprehension of context and
further hindering emotion prediction. To this end, we design a novel speaker
modeling scheme that explores intra- and inter-speaker dependencies jointly in
a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED)
framework for ERC, which fully exploits speaker information for the decoding of
emotion. We use different existing methods as the conversational context
encoder of our framework, showing the high scalability and flexibility of the
proposed framework. Experimental results demonstrate the superiority and
effectiveness of SGED.

本文提出了一种探索语音信息中内部和外部说话者依赖关系的新型说话者建模方案，并使用 Speaker-Guided 编码器 - 解码器框架来预测情感标签，证明了其在情感识别中的优越性和有效性。