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Machine Learning Classification of Dimensional Affect From Continuous Voice Signals

Affective Science

Affective Voice Analysis & Prosody Research

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Affective Science › Affective Voice Analysis & Prosody Research

Machine Learning Classification of Dimensional Affect From Continuous Voice Signals

This study develops deep learning and supervised learning models that map continuous acoustic features to dimensional representations of affect (valence, arousal, dominance) rather than discrete categories. The research advances computational affective science by capturing the nuanced, graded nature of emotional expression in naturalistic speech.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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