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  1. Proceedings of the Workshop on Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction (MA3HMI '16)
  2. Attitude recognition of video bloggers using audio-visual descriptors
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Analysis of gesture frequency and amplitude as a function of personality in virtual agents
Annotation and analysis of listener's engagement based on multi-modal behaviors
Fitmirror: a smart mirror for positive affect in everyday user morning routines
Deictic gestures in coaching interactions
Assessment of users' interests in multimodal dialog based on exchange unit
Increasing robustness of multimodal interaction via individual interaction histories
Automatic annotation of gestural units in spontaneous face-to-face interaction
Attitude recognition of video bloggers using audio-visual descriptors
Body movements and laughter recognition: experiments in first encounter dialogues
On data driven parametric backchannel synthesis for expressing attentiveness in conversational agents

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Attitude recognition of video bloggers using audio-visual descriptors

Content Provider ACM Digital Library
Author Haider, Fasih Cerrato, Loredana Sundberg Luz, Saturnino Campbell, Nick
Abstract In social media, vlogs (video blogs) are a form of unidirectional communication, where the vloggers (video bloggers) convey their messages (opinions, thoughts, etc.) to a potential audience which cannot give them feedback in real time. In this kind of communication, the non-verbal behaviour and personality impression of a video blogger tends to influence viewers' attention because non-verbal cues are correlated with the messages conveyed by a vlogger. In this study, we use the acoustic and visual features (body movements that are captured by low-level visual descriptors) to predict the six different attitudes (amusement, enthusiasm, friendliness, frustration, impatience and neutral) annotated in the speech of 10 video bloggers. The automatic detection of attitude can be helpful in a scenario where a machine has to automatically provide feedback to bloggers about their performance in terms of the extent to which they manage to engage the audience by displaying certain attitudes. Attitude recognition models are trained using the random forest classifier. Results show that: 1) acoustic features provide better accuracy than the visual features, 2) while fusion of audio and visual features does not increase overall accuracy, it improves the results for some attitudes and subjects, and 3) densely extracted histograms of flow provide better results than other visual descriptors. A three-class (positive, negative and neutral attitudes) problem has also been defined. Results for this setting show that feature fusion degrades overall classifier accuracy, and the classifiers perform better on the original six-class problem than on the three-class setting.
Starting Page 38
Ending Page 42
Page Count 5
File Format PDF
ISBN 9781450345620
DOI 10.1145/3011263.3011270
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-11-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Video blogs Emotion recognition Non-verbal behavior analysis Social media Vloggers Instructional advice Attitude recogniton Expressive speech analysis
Content Type Text
Resource Type Article
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