Shifting Relational Paradigms for Affective Computing: Affective Resonance, Vitality Affects, and Vocal Interaction Fields
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Abstract (arXiv)
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.
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References · 9
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 666
- 2023emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representationcited by 205
- 2024Self-Supervised Speech Representations are More Phonetic than Semanticcited by 71
- 2022Speech Emotion Recognition using Self-Supervised Featurescited by 53
- 2025Recent Advances in Discrete Speech Tokens: A Reviewcited by 43
- 2023Unsupervised Auditory and Semantic Entrainment Models with Deep Neural Networkscited by 1
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