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Challenging affective computing鈥檚 isolated-speaker paradigm, the authors formulate a relational framework that treats multi-party vocal interaction fields as the primary unit of affective analysis. To detect emergent expressive coupling, they leverage continuous self-supervised speech representations across multi-party conversational regimes alongside null-calibrated negative controls. Their findings demonstrate that vocal affective coupling is regime-specific and concentrated at sub-second timescales, collapsing under exclusive-speech controls to confirm that conversational affect is an emergent relational dynamic rather than an isolated state.
Human affect is fundamentally relational: vocal expressive coupling operates across speakers at sub-second timescales, exposing the severe limitations of modeling individual emotional states in isolation.
Affective computing has largely followed an individual-state paradigm, extracting discrete emotion labels or arousal/valence from isolated speakers. We argue this framing is incomplete for interaction. Drawing on affective resonance and vitality-contour accounts, we propose a relational framework in which the primary unit of affective analysis is the interactional field constituted within vocal dynamics. As a proof of concept, we present a preliminary empirical study using continuous self-supervised speech representations to detect directional expressive coupling in multi-party conversation. Coupling is regime-specific, concentrated at sub-second timescales, and collapses under exclusive-speech negative controls, consistent with a relational account of affective dynamics. We introduce design frameworks for Artificial Affective Resonance Intelligence grounded in Affective Resonance Dynamic Ontologies, supported by null-calibrated directional coupling analyses across interaction regimes.
