Key Findings in Emotional Prosody: Theoretical Frameworks and Empirical Advances
Journal: Journal of Higher Education Research DOI: 10.32629/jher.v6i3.4117
Abstract
Emotional prosody is integral to human communication and AI-driven affective technologies. A systematic review is needed to clarify key findings and guide future research. This paper examines major frameworks and integrates recent empirical advances in acoustic-perceptual mechanisms. The review aims to provide an overview of the main findings in emotional prosody research and to offer insights that may inspire future investigations. Further exploration of dynamic, culturally grounded, and multimodal perspectives will be essential in advancing the field.
Keywords
emotional prosody; acoustic features; speech perception; prospects
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[2] Banse & Scherer 1996 Acoustic profiles in vocal emotion expression. Journal of Personality and Social Psychology.
[3] Russell 1980 A circumplex model of affect. Journal of personality and social psychology.
[4] Juslin & Laukka 2003 Communication of emotions in vocal expression and music performance: Different channels, same code? Psychological bulletin.
[5] Scherer 2009 The dynamic architecture of emotion: Evidence for the component process model. Cognition and emotion.
[6] Busso et al. 2008 IEMOCAP: Interactive emotional dyadic motion capture database. Language resources and evaluation.
[7] Hammerschmidt & Jürgens 2007 Acoustical correlates of affective prosody. Journal of voice.
[8] Eyben et al. 2015 The GeMAPS for voice research and affective computing. IEEE transactions on affective computing.
[9] Pell et al. 2009 Factors in the recognition of vocally expressed emotions: A comparison of four languages. Journal of Phonetics.
[10] Van Rijn & Larrouy-Maestri. 2023 Modelling individual and cross-cultural variation in the mapping of emotions to speech prosody. Nature Human Behaviour.
[11] Liebenthal et al. 2016 The language, tone and prosody of emotions: neural substrates and dynamics of spoken-word emotion perception. Frontiers in neuroscience.
[12] Mittal et al. 2020 M3er: Multiplicative multimodal emotion recognition using facial, textual, and speech cues. Proceedings of the AAAI conference on artificial intelligence.
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