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- W4225103667 abstract "With the growing prevalence of affective computing applications, Automatic Emotion Recognition (AER) technologies have garnered attention in both research and industry settings. Initially limited to speech-based applications, AER technologies now include analysis of facial landmarks to provide predicted probabilities of a common subset of emotions (e.g., anger, happiness) for faces observed in an image or video frame. In this paper, we study the relationship between AER outputs and self-reports of affect employed by prior work, in the context of information work at a technology company. We compare the continuous observed emotion output from an AER tool to discrete reported affect obtained via a one-day combined tool-use and diary study (N = 15). We provide empirical evidence showing that these signals do not completely align, and find that using additional workplace context only improves alignment up to 58.6%. These results suggest affect must be studied in the context it is being expressed, and observed emotion signal should not replace internal reported affect for affective computing applications." @default.
- W4225103667 created "2022-04-30" @default.
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- W4225103667 date "2022-04-29" @default.
- W4225103667 modified "2023-10-14" @default.
- W4225103667 title "“I Didn’t Know I Looked Angry”: Characterizing Observed Emotion and Reported Affect at Work" @default.
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- W4225103667 doi "https://doi.org/10.1145/3491102.3517453" @default.
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