15,899 research outputs found

    Audiovisual integration of emotional signals from others' social interactions

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    Audiovisual perception of emotions has been typically examined using displays of a solitary character (e.g., the face-voice and/or body-sound of one actor). However, in real life humans often face more complex multisensory social situations, involving more than one person. Here we ask if the audiovisual facilitation in emotion recognition previously found in simpler social situations extends to more complex and ecological situations. Stimuli consisting of the biological motion and voice of two interacting agents were used in two experiments. In Experiment 1, participants were presented with visual, auditory, auditory filtered/noisy, and audiovisual congruent and incongruent clips. We asked participants to judge whether the two agents were interacting happily or angrily. In Experiment 2, another group of participants repeated the same task, as in Experiment 1, while trying to ignore either the visual or the auditory information. The findings from both experiments indicate that when the reliability of the auditory cue was decreased participants weighted more the visual cue in their emotional judgments. This in turn translated in increased emotion recognition accuracy for the multisensory condition. Our findings thus point to a common mechanism of multisensory integration of emotional signals irrespective of social stimulus complexity

    AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition

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    The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively

    AVEC 2017--Real-life depression, and affect recognition workshop and challenge

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    The Audio/Visual Emotion Challenge and Workshop (AVEC 2017) “Real-life depression, and affect” will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and emotion analysis, with all participants competing under strictly the same conditions. .e goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the depression and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of the various approaches to depression and emotion recognition from real-life data. .is paper presents the novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline system on the two proposed tasks: dimensional emotion recognition (time and value-continuous), and dimensional depression estimation (value-continuous)

    Degraded visual and auditory input individually impair audiovisual emotion recognition from speech-like stimuli, but no evidence for an exacerbated effect from combined degradation

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    Emotion recognition requires optimal integration of the multisensory signals from vision and hearing. A sensory loss in either or both modalities can lead to changes in integration and related perceptual strategies. To investigate potential acute effects of combined impairments due to sensory information loss only, we degraded the visual and auditory information in audiovisual video-recordings, and presented these to a group of healthy young volunteers. These degradations intended to approximate some aspects of vision and hearing impairment in simulation. Other aspects, related to advanced age, potential health issues, but also long-term adaptation and cognitive compensation strategies, were not included in the simulations. Besides accuracy of emotion recognition, eye movements were recorded to capture perceptual strategies. Our data show that emotion recognition performance decreases when degraded visual and auditory information are presented in isolation, but simultaneously degrading both modalities does not exacerbate these isolated effects. Moreover, degrading the visual information strongly impacts recognition performance and on viewing behavior. In contrast, degrading auditory information alongside normal or degraded video had little (additional) effect on performance or gaze. Nevertheless, our results hold promise for visually impaired individuals, because the addition of any audio to any video greatly facilitates performance, even though adding audio does not completely compensate for the negative effects of video degradation. Additionally, observers modified their viewing behavior to degraded video in order to maximize their performance. Therefore, optimizing the hearing of visually impaired individuals and teaching them such optimized viewing behavior could be worthwhile endeavors for improving emotion recognition

    Eyes on Emotion:Dynamic Gaze Allocation During Emotion Perception From Speech-Like Stimuli

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    The majority of emotional expressions used in daily communication are multimodal and dynamic in nature. Consequently, one would expect that human observers utilize specific perceptual strategies to process emotions and to handle the multimodal and dynamic nature of emotions. However, our present knowledge on these strategies is scarce, primarily because most studies on emotion perception have not fully covered this variation, and instead used static and/or unimodal stimuli with few emotion categories. To resolve this knowledge gap, the present study examined how dynamic emotional auditory and visual information is integrated into a unified percept. Since there is a broad spectrum of possible forms of integration, both eye movements and accuracy of emotion identification were evaluated while observers performed an emotion identification task in one of three conditions: audio-only, visual-only video, or audiovisual video. In terms of adaptations of perceptual strategies, eye movement results showed a shift in fixations toward the eyes and away from the nose and mouth when audio is added. Notably, in terms of task performance, audio-only performance was mostly significantly worse than video-only and audiovisual performances, but performance in the latter two conditions was often not different. These results suggest that individuals flexibly and momentarily adapt their perceptual strategies to changes in the available information for emotion recognition, and these changes can be comprehensively quantified with eye tracking

    Speech-based recognition of self-reported and observed emotion in a dimensional space

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    The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance

    Emotional Cues during Simultaneous Face and Voice Processing: Electrophysiological Insights

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    Both facial expression and tone of voice represent key signals of emotional communication but their brain processing correlates remain unclear. Accordingly, we constructed a novel implicit emotion recognition task consisting of simultaneously presented human faces and voices with neutral, happy, and angry valence, within the context of recognizing monkey faces and voices task. To investigate the temporal unfolding of the processing of affective information from human face-voice pairings, we recorded event-related potentials (ERPs) to these audiovisual test stimuli in 18 normal healthy subjects; N100, P200, N250, P300 components were observed at electrodes in the frontal-central region, while P100, N170, P270 were observed at electrodes in the parietal-occipital region. Results indicated a significant audiovisual stimulus effect on the amplitudes and latencies of components in frontal-central (P200, P300, and N250) but not the parietal occipital region (P100, N170 and P270). Specifically, P200 and P300 amplitudes were more positive for emotional relative to neutral audiovisual stimuli, irrespective of valence, whereas N250 amplitude was more negative for neutral relative to emotional stimuli. No differentiation was observed between angry and happy conditions. The results suggest that the general effect of emotion on audiovisual processing can emerge as early as 200 msec (P200 peak latency) post stimulus onset, in spite of implicit affective processing task demands, and that such effect is mainly distributed in the frontal-central region
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