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EmoNet: Deep Learning-based Emotion Climate Recognition Using Peers' Conversational Speech, Affect Dynamics, and

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study introduces EmoNet, an AI model for recognizing collective emotion climates in social interactions. EmoNet integrates speech and physiological data, improving emotion recognition accuracy in conversations.

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    Area of Science:

    • Artificial Intelligence
    • Affective Computing
    • Human-Computer Interaction

    Background:

    • Understanding social emotional dynamics is key for interpretation.
    • Current emotion recognition systems often overlook collective emotional climates.
    • Recognizing group emotions in real-time interactions remains a challenge.

    Purpose of the Study:

    • To develop an AI model, EmoNet, for identifying collective emotional climates.
    • To go beyond traditional emotion recognition by integrating multiple data streams.
    • To provide a holistic approach to understanding group affect in conversations.

    Main Methods:

    • EmoNet utilizes Mel-frequency cepstral coefficients (MFCCs) for speech feature extraction.
    • A Temporal Convolutional Network (TCN) is employed for deep feature learning.
    • Integration of affect dynamics with physiological inputs (heart rate, electrodermal activity) for comprehensive analysis.

    Main Results:

    • EmoNet demonstrated high accuracy in classifying arousal (87.82%) and valence (83.79%) on the K-EmoCon dataset.
    • The model successfully captures nuanced emotional dynamics within group interactions.
    • Performance metrics indicate significant advancements in collective emotion recognition.

    Conclusions:

    • EmoNet offers a novel approach to understanding and influencing emotion climates in conversations.
    • The model has potential applications in healthcare and human-computer interaction for improved social dynamics.
    • This research highlights the importance of multimodal data for accurate collective emotion recognition.