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Lightweight CNN-LSTM model EmoNet achieves 96.75% accuracy in speech emotion recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EmoNet, a lightweight deep learning model designed for speech emotion recognition. This model utilizes a combination of a depthwise separable CNN with squeeze-and-excitation attention and a Bidirectional LSTM, along with multi-head attention pooling. EmoNet achieves a high accuracy of 96.75% on a combined dataset of TESS, RAVDESS, and SAVEE, while maintaining a small parameter count of 4.11 million, making it suitable for resource-constrained environments. AI

IMPACT This lightweight model could enable more efficient speech emotion recognition in real-time applications and on devices with limited computational power.

RANK_REASON The cluster contains a research paper detailing a new model for speech emotion recognition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Lightweight CNN-LSTM model EmoNet achieves 96.75% accuracy in speech emotion recognition

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The cluster contains a research paper detailing a new model for speech emotion recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Muhammad Moiz Khalid, Abdur Rehman Tariq, Muhammad Okasha Yamin ·

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

    arXiv:2610.33977v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) is a fundamental task in affective computing that enables applications such as mental health assessment, human-computer interaction, and automated customer support systems. Accurate emotion classif…

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