NDLI: Emotion spotting: discovering regions of evidence in audio-visual emotion expressions

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Emotion spotting: discovering regions of evidence in audio-visual emotion expressions

Content Provider	ACM Digital Library
Author	Kim, Yelin Provost, Emily Mower
Abstract	Research has demonstrated that humans require different amounts of information, over time, to accurately perceive emotion expressions. This varies as a function of emotion classes. For example, recognition of happiness requires a longer stimulus than recognition of anger. However, previous automatic emotion recognition systems have often overlooked these differences. In this work, we propose a data-driven framework to explore patterns (timings and durations) of emotion evidence, specific to individual emotion classes. Further, we demonstrate that these patterns vary as a function of which modality (lower face, upper face, or speech) is examined, and consistent patterns emerge across different folds of experiments. We also show similar patterns across emotional corpora (IEMOCAP and MSP-IMPROV). In addition, we show that our proposed method, which uses only a portion of the data (59% for the IEMOCAP), achieves comparable accuracy to a system that uses all of the data within each utterance. Our method has a higher accuracy when compared to a baseline method that randomly chooses a portion of the data. We show that the performance gain of the method is mostly from prototypical emotion expressions (defined as expressions with rater consensus). The innovation in this study comes from its understanding of how multimodal cues reveal emotion over time. .
Starting Page	92
Ending Page	99
Page Count	8
File Format	PDF
ISBN	9781450345569
DOI	10.1145/2993148.2993151
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Audio-visual Emotion classification Emotion spotting Emotion Temporal evidence
Content Type	Text
Resource Type	Article

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