# Key-Sparse Transformer for Multimodal Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2106.11532 · HTML: https://mlsanity.com/paper/2106.11532/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2021.

## Built on by (most cited first, 10 of 10)

- [SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing](https://mlsanity.com/paper/2302.14638/) (2023)
- [Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition](https://mlsanity.com/paper/2307.10757/) (2023)
- [GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition](https://mlsanity.com/paper/2306.07848/) (2023)
- [Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognition](https://mlsanity.com/paper/2302.09856/) (2023)
- [DWFormer: Dynamic Window transFormer for Speech Emotion Recognition](https://mlsanity.com/paper/2303.01694/) (2023)
- [MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention](https://mlsanity.com/paper/2404.13509/) (2024)
- [SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning](https://mlsanity.com/paper/2206.13101/) (2022)
- [Leveraging Label Information for Multimodal Emotion Recognition](https://mlsanity.com/paper/2309.02106/) (2023)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Design of an Expression Recognition Solution Based on the Global Channel-Spatial Attention Mechanism and Proportional Criterion Fusion](https://mlsanity.com/paper/2503.11935/) (2025)

## Abstract (arXiv)

Speech emotion recognition is a challenging research topic that plays a critical role in human-computer interaction. Multimodal inputs further improve the performance as more emotional information is used. However, existing studies learn all the information in the sample while only a small portion of it is about emotion. The redundant information will become noises and limit the system performance. In this paper, a key-sparse Transformer is proposed for efficient emotion recognition by focusing more on emotion related information. The proposed method is evaluated on the IEMOCAP and LSSED. Experimental results show that the proposed method achieves better performance than the state-of-the-art approaches.