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

TL;DR

This paper introduces Light-SERNet, a lightweight fully convolutional neural network designed for speech emotion recognition, optimized for embedded systems with limited resources, achieving high accuracy with fewer computational demands.

Contribution

The paper presents a novel, efficient FCNN architecture with parallel feature extraction paths that outperforms larger models on standard datasets.

Findings

Smaller model size than state-of-the-art

Higher accuracy on IEMOCAP and EMO-DB datasets

Effective feature extraction with parallel paths

Abstract

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.