Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition
Audio and Speech Processing
2025-04-16 v1 Artificial Intelligence
Machine Learning
Sound
Machine Learning
Abstract
This paper proposes a Residual Convolutional Neural Network (ResNet) based on speech features and trained under Focal Loss to recognize emotion in speech. Speech features such as Spectrogram and Mel-frequency Cepstral Coefficients (MFCCs) have shown the ability to characterize emotion better than just plain text. Further Focal Loss, first used in One-Stage Object Detectors, has shown the ability to focus the training process more towards hard-examples and down-weight the loss assigned to well-classified examples, thus preventing the model from being overwhelmed by easily classifiable examples.
Cite
@article{arxiv.1906.05682,
title = {Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition},
author = {Suraj Tripathi and Abhay Kumar and Abhiram Ramesh and Chirag Singh and Promod Yenigalla},
journal= {arXiv preprint arXiv:1906.05682},
year = {2025}
}
Comments
Accepted in CICLing 2019