This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.
The Interactive Emotional Dyadic Motion Capture (IEMOCAP) @cite_5 dataset is one of the most used corpora for training emotion recognition systems. It contains 12 hours of audiovisual recordings of dialogue sessions between two actors. Each dialogue session is split into utterances and each utterance is annotated by five different annotators. Annotators evaluated the arousal valence and categorical emotion (Neutral, Happiness, Sadness, Anger, Surprise, Fear, Disgust, Frustration and Excitement) of each utterance. The annotators were instructed to watch and annotate the entire dialogue in sequence, in order to keep the contextual information in their annotations. This avoids improbable transitions in one dialogue (from frustrated to satisfied in a single sentence). Although this dataset is used by many researchers, it is heavily limited by the data collection method: The recordings happened in an indoor scenario, and although the subjects performed scripted and non-scripted dialogues, the instructions given to the subjects limited the types of emotions that can be expressed.
@cite_5
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Since emotions are expressed through a combination of verbal and non-verbal channels, a joint analysis of speech and gestures is required to understand expressive human communication. To facilitate such investigations, this paper describes a new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). This database was recorded from ten actors in dyadic sessions with markers on the face, head, and hands, which provide detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios. The actors performed selected emotional scripts and also improvised hypothetical scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state). The corpus contains approximately 12 h of data. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication.