LIGHT-SERNET: A Lightweight Fully Convolutional Neural Network for Speech Emotion Recognition

Arya Aftab; Alireza Morsali; Shahrokh Ghaemmaghami; Benoît Champagne

ICASSP 2022accepted0 citations

LIGHT-SERNET: A Lightweight Fully Convolutional Neural Network for Speech Emotion Recognition

Arya Aftab, Alireza Morsali, Shahrokh Ghaemmaghami, Benoît Champagne

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 hard-ware 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 a higher performance on the IEMOCAP and EMO-DB datasets. The source code is available https://github.com/AryaAftab/LIGHT-SERNET

BibTeX

@inproceedings{icassp2022_lightsernetaligh,
  title = {LIGHT-SERNET: A Lightweight Fully Convolutional Neural Network for Speech Emotion Recognition},
  author = {Arya Aftab and Alireza Morsali and Shahrokh Ghaemmaghami and Benoît Champagne},
  booktitle = {ICASSP 2022},
  year = {2022}
}