# Attentive Modality Hopping Mechanism for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/1912.00846 · HTML: https://mlsanity.com/paper/1912.00846/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Information](https://mlsanity.com/paper/2203.15326/) (2022)
- [HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2401.05698/) (2024)
- [Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?](https://mlsanity.com/paper/2202.09263/) (2022)
- [MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild](https://mlsanity.com/paper/2404.09010/) (2024)
- [A Two-stage Multi-modal Affect Analysis Framework for Children with Autism Spectrum Disorder](https://mlsanity.com/paper/2106.09199/) (2021)
- [An Empirical Study and Improvement for Speech Emotion Recognition](https://mlsanity.com/paper/2304.03899/) (2023)

## Abstract (arXiv)

In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by fusing the knowledge from the various modalities independently for performing emotion classification. In contrast to these approaches, we tackle the problem by introducing an attention mechanism to combine the information. In this regard, we first apply a neural network to obtain hidden representations of the modalities. Then, the attention mechanism is defined to select and aggregate important parts of the video data by conditioning on the audio and text data. Furthermore, the attention mechanism is again applied to attend important parts of the speech and textual data, by considering other modality. Experiments are performed on the standard IEMOCAP dataset using all three modalities (audio, text, and video). The achieved results show a significant improvement of 3.65% in terms of weighted accuracy compared to the baseline system.