# Compact Graph Architecture for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2008.02063 · HTML: https://mlsanity.com/paper/2008.02063/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data](https://mlsanity.com/paper/2202.00097/) (2022)
- [Heterogeneous Graph Learning for Acoustic Event Classification](https://mlsanity.com/paper/2303.02665/) (2023)
- [Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition](https://mlsanity.com/paper/2109.07149/) (2021)
- [Graph Neural Networks for Parkinsons Disease Detection](https://mlsanity.com/paper/2409.07884/) (2024)
- [A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition](https://mlsanity.com/paper/2207.00940/) (2022)
- [Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques](https://mlsanity.com/paper/2308.04517/) (2023)
- [Hierarchical Graph Neural Network for Compressed Speech Steganalysis](https://mlsanity.com/paper/2507.21591/) (2025)
- [Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition](https://mlsanity.com/paper/2509.20373/) (2025)

## Abstract (arXiv)

We propose a deep graph approach to address the task of speech emotion recognition. A compact, efficient and scalable way to represent data is in the form of graphs. Following the theory of graph signal processing, we propose to model speech signal as a cycle graph or a line graph. Such graph structure enables us to construct a Graph Convolution Network (GCN)-based architecture that can perform an accurate graph convolution in contrast to the approximate convolution used in standard GCNs. We evaluated the performance of our model for speech emotion recognition on the popular IEMOCAP and MSP-IMPROV databases. Our model outperforms standard GCN and other relevant deep graph architectures indicating the effectiveness of our approach. When compared with existing speech emotion recognition methods, our model achieves comparable performance to the state-of-the-art with significantly fewer learnable parameters (~30K) indicating its applicability in resource-constrained devices.