Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Estimating a silent participant's degree of engagement and his role within a group discussion can be challenging, as there are no speech related cues available at the given time. Having this information available, hover, can provide important insights into the dynamics of the group as a whole. ,  study the classification of listeners into several categories (attentive listener, side participant and bystander).  devised a thin-sliced perception test where subjects re asked to assess listener roles and engagement levels in 15-second video-clips taken from a corpus of group interviews. Results show that humans are usually able to assess silent participant roles. Using the annotation to identify from a set of multimodal low-level features, such as past speaking activity, backchannels (both visual and verbal), as ll as gaze patterns,  could identify the features which are able to distinguish beten different listener categories. Moreover, the results show that many of the audio-visual effects observed on listeners in dyadic interactions, also hold for multi-party interactions. A preliminary classifier achieves an accuracy of 64 %.
['Catharine Oertel', 'Kenneth Alberto Funes Mora', 'Joakim Gustafson', 'Jean-Marc Odobez']
I am writing a research paper on a idea Deciphering the Silent Participant: On the Use of Audio-Visual Cues for the Classification of Listener Categories in Group Discussions
[{"from": "human", "value": "I am writing a research paper on a idea Deciphering the Silent Participant: On the Use of Audio-Visual Cues for the Classification of Listener Categories in Group Discussions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Estimating a silent participant's degree of engagement and his role within a group discussion can be challenging, as there are no speech related cues available at the given time. Having this information available, hover, can provide important insights into the dynamics of the group as a whole. ,  study the classification of listeners into several categories (attentive listener, side participant and bystander).  devised a thin-sliced perception test where subjects re asked to assess listener roles and engagement levels in 15-second video-clips taken from a corpus of group interviews. Results show that humans are usually able to assess silent participant roles. Using the annotation to identify from a set of multimodal low-level features, such as past speaking activity, backchannels (both visual and verbal), as ll as gaze patterns,  could identify the features which are able to distinguish beten different listener categories. Moreover, the results show that many of the audio-visual effects observed on listeners in dyadic interactions, also hold for multi-party interactions. A preliminary classifier achieves an accuracy of 64 %."}]