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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.18908 |
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| _version_ | 1866908790450814976 |
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| author | Hizabri, Marouane El Bezzaz, Abdelfattah Hayoukane, Ismail Taki, Youssef |
| author_facet | Hizabri, Marouane El Bezzaz, Abdelfattah Hayoukane, Ismail Taki, Youssef |
| contents | Speech Emotion Recognition systems often use static features like Mel-Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR), and Root Mean Square Energy (RMSE). Because of this, they can misclassify emotions when there is acoustic noise in vocal signals. To address this, we added dynamic features using Dynamic Spectral features (Deltas and Delta-Deltas) along with the Kalman Smoothing algorithm. This approach reduces noise and improves emotion classification. Since emotion changes over time, the Kalman Smoothing filter also helped make the classifier outputs more stable. Tests on the RAVDESS dataset showed that this method achieved a state-of-the-art accuracy of 87\% and reduced misclassification between emotions with similar acoustic features |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18908 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Speech Emotion Recognition using Dynamic Spectral Features and Kalman Smoothing Hizabri, Marouane El Bezzaz, Abdelfattah Hayoukane, Ismail Taki, Youssef Sound Machine Learning Audio and Speech Processing Speech Emotion Recognition systems often use static features like Mel-Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR), and Root Mean Square Energy (RMSE). Because of this, they can misclassify emotions when there is acoustic noise in vocal signals. To address this, we added dynamic features using Dynamic Spectral features (Deltas and Delta-Deltas) along with the Kalman Smoothing algorithm. This approach reduces noise and improves emotion classification. Since emotion changes over time, the Kalman Smoothing filter also helped make the classifier outputs more stable. Tests on the RAVDESS dataset showed that this method achieved a state-of-the-art accuracy of 87\% and reduced misclassification between emotions with similar acoustic features |
| title | Enhancing Speech Emotion Recognition using Dynamic Spectral Features and Kalman Smoothing |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.18908 |