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arXiv Machine Learning By Muhammad Moiz Khalid, Abdur Rehman Tariq, Muhammad Okasha Yamin

EmoNet: A Lightweight Attention-Based CNN for Speech Emotion Recognition

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EmoNet is a lightweight attention‑based CNN framework designed for speech emotion recognition across seven categories. It combines a four‑block depthwise separable CNN encoder with squeeze‑and‑excitation attention, residual connections, a Bidirectional LSTM, and multi‑head attention pooling, using a 134‑channel acoustic feature set and data augmentation to enhance robustness. On a combined TESS, RAVDESS, and SAVEE dataset, EmoNet achieves 96.75% test accuracy with only 4.11 million parameters, outperforming a reproduced baseline and competing with larger state‑of‑the‑art models.

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