# Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data

arXiv: https://arxiv.org/abs/1805.06606 · HTML: https://mlsanity.com/paper/1805.06606/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 4 of 4)

- [Multistage linguistic conditioning of convolutional layers for speech emotion recognition](https://mlsanity.com/paper/2110.06650/) (2021)
- [MMER: Multimodal Multi-task Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2203.16794/) (2022)
- [Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss](https://mlsanity.com/paper/2207.11482/) (2022)
- [Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment](https://mlsanity.com/paper/2412.20821/) (2024)

## Abstract (arXiv)

Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset.