EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915536704634880 |
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| author | Muppidi, Akshay Radfar, Martin |
| author_facet | Muppidi, Akshay Radfar, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition Muppidi, Akshay Radfar, Martin Sound Machine Learning 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. |
| title | EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2510.06072 |