EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition

Akshay Muppidi, Martin Radfar
10/7/2025
cs.SDcs.LG

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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.

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Cite this paper

@article{muppidi2025emohrnet,
  title  = {EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition},
  author = {Akshay Muppidi and Martin Radfar},
  year   = {2025},
  eprint = {2510.06072},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.06072v1}
}

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