Conversational Emotion Analysis via Attention Mechanisms

Zheng Lian, Jianhua Tao, Bin Liu, Jian Huang


Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis. The attention mechanism is applied to the fusion of the acoustic and lexical features. Then these fusion representations are fed into the self-attention based bi-directional gated recurrent unit (GRU) layer to capture long-term contextual information. To imitate real interaction patterns of different speakers, speaker embeddings are also utilized as additional inputs to distinguish the speaker identities during conversational dialogs. To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that our method shows absolute 2.42% performance improvement over the state-of-the-art strategies.


 DOI: 10.21437/Interspeech.2019-1577

Cite as: Lian, Z., Tao, J., Liu, B., Huang, J. (2019) Conversational Emotion Analysis via Attention Mechanisms. Proc. Interspeech 2019, 1936-1940, DOI: 10.21437/Interspeech.2019-1577.


@inproceedings{Lian2019,
  author={Zheng Lian and Jianhua Tao and Bin Liu and Jian Huang},
  title={{Conversational Emotion Analysis via Attention Mechanisms}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={1936--1940},
  doi={10.21437/Interspeech.2019-1577},
  url={http://dx.doi.org/10.21437/Interspeech.2019-1577}
}