Abstract:Social signal processing is a methodology that is used to infer human inner states, including attitudes, sentiments and impressions, from verbal and nonverbal multimodal information. The difficulty in training a social signal recognition model is that the ground-truth (target) labels given by multiple coders often disagree because the annotation of social signals such as sentiments is a subjective and ambiguous task. We introduce weakly supervised learning (WSL) algorithms to such an inaccurate supervision set… Show more
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