Journal article

(Not) Hearing Happiness: Predicting Fluctuations in Happy Mood From Acoustic Cues Using Machine Learning

Aaron C Weidman, Jessie Sun, Simine Vazire, Jordi Quoidbach, Lyle H Ungar, Elizabeth W Dunn

Emotion | AMER PSYCHOLOGICAL ASSOC | Published : 2020

Abstract

Recent popular claims surrounding virtual assistants suggest that computers will soon be able to hear our emotions. Supporting this possibility, promising work has harnessed big data and emergent technologies to automatically predict stable levels of one specific emotion, happiness, at the community (e.g., counties) and trait (i.e., people) levels. Furthermore, research in affective science has shown that nonverbal vocal bursts (e.g., sighs, gasps) and specific acoustic features (e.g., pitch, energy) can differentiate between distinct emotions (e.g., anger, happiness) and that machine-learning algorithms can detect these differences. Yet, to our knowledge, no work has tested whether computer..

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