EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition
Abstract
Speech emotion recognition (SER) is pivotal for enhancing human-machine interactions. This paper introduces "EmoHRNet", a novel adaptation of High-Resolution Networks (HRNet) tailored for SER. The HRNet structure is designed to maintain high-resolution representations from the initial to the final layers. By transforming audio samples into spectrograms, EmoHRNet leverages the HRNet architecture to extract high-level features. EmoHRNet's unique architecture maintains high-resolution representations throughout, capturing both granular and overarching emotional cues from speech signals. The model outperforms leading models, achieving accuracies of 92.45% on RAVDESS, 80.06% on IEMOCAP, and 92.77% on EMOVO. Thus, we show that EmoHRNet sets a new benchmark in the SER domain.
Cite
@article{arxiv.2510.06072,
title = {EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition},
author = {Akshay Muppidi and Martin Radfar},
journal= {arXiv preprint arXiv:2510.06072},
year = {2025}
}