# Conversational Emotion Analysis via Attention Mechanisms

arXiv: https://arxiv.org/abs/1910.11263 · HTML: https://mlsanity.com/paper/1910.11263/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation](https://mlsanity.com/paper/2203.02177/) (2022)
- [Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2208.07589/) (2022)
- [Empathy Through Multimodality in Conversational Interfaces](https://mlsanity.com/paper/2405.04777/) (2024)
- [A study on cross-corpus speech emotion recognition and data augmentation](https://mlsanity.com/paper/2201.03511/) (2022)
- [Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023](https://mlsanity.com/paper/2309.07925/) (2023)
- [Audio-Guided Fusion Techniques for Multimodal Emotion Analysis](https://mlsanity.com/paper/2409.05007/) (2024)
- [An Empirical Study and Improvement for Speech Emotion Recognition](https://mlsanity.com/paper/2304.03899/) (2023)

## Abstract (arXiv)

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.