# EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2510.06072 · HTML: https://mlsanity.com/paper/2510.06072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Speech Emotion Recognition with Dual-Sequence LSTM Architecture](https://mlsanity.com/paper/1910.08874/) (2019)
- [Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition](https://mlsanity.com/paper/2211.08233/) (2022)
- [Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition](https://mlsanity.com/paper/2110.03435/) (2021)
- [Speech Emotion Recognition Using Quaternion Convolutional Neural Networks](https://mlsanity.com/paper/2111.00404/) (2021)
- [CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2207.10644/) (2022)
- [GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition](https://mlsanity.com/paper/2210.15834/) (2022)

## Abstract (arXiv)

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.