# Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

arXiv: https://arxiv.org/abs/2110.03435 · HTML: https://mlsanity.com/paper/2110.03435/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2021.

## Built on by (most cited first, 14 of 14)

- [Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition](https://mlsanity.com/paper/2211.08233/) (2022)
- [GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition](https://mlsanity.com/paper/2306.07848/) (2023)
- [Disentangling Prosody Representations with Unsupervised Speech Reconstruction](https://mlsanity.com/paper/2212.06972/) (2022)
- [Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP](https://mlsanity.com/paper/2304.00860/) (2023)
- [Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2308.02190/) (2023)
- [MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition](https://mlsanity.com/paper/2308.04025/) (2023)
- [Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations](https://mlsanity.com/paper/2309.04849/) (2023)
- [AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition](https://mlsanity.com/paper/2607.25289/) (2026)
- [A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions](https://mlsanity.com/paper/2401.00536/) (2023)
- [Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition](https://mlsanity.com/paper/2409.04007/) (2024)
- [SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition](https://mlsanity.com/paper/2502.00310/) (2025)
- [Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs](https://mlsanity.com/paper/2505.07041/) (2025)
- [EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition](https://mlsanity.com/paper/2510.06072/) (2025)
- [Distilled HuBERT for Mobile Speech Emotion Recognition: A Cross-Corpus Validation Study](https://mlsanity.com/paper/2512.23435/) (2025)

## Abstract (arXiv)

Detecting emotions directly from a speech signal plays an important role in effective human-computer interactions. Existing speech emotion recognition models require massive computational and storage resources, making them hard to implement concurrently with other machine-interactive tasks in embedded systems. In this paper, we propose an efficient and lightweight fully convolutional neural network for speech emotion recognition in systems with limited hardware resources. In the proposed FCNN model, various feature maps are extracted via three parallel paths with different filter sizes. This helps deep convolution blocks to extract high-level features, while ensuring sufficient separability. The extracted features are used to classify the emotion of the input speech segment. While our model has a smaller size than that of the state-of-the-art models, it achieves higher performance on the IEMOCAP and EMO-DB datasets.