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We address the problem of recognizing, in dynamic meetings in which people do not remain seated all the time, the visual focus of attention (VFOA) of seated people from their head pose and contextual activity cues. We propose a model that comprises the VFOA of a meeting participant as the hidden state, and his head pose as the observation. To account for the presence of moving visual targets due to...
Speaker diarization is originally defined as the task of determining ldquowho spoke whenrdquo given an audio track and no other prior knowledge of any kind. The following article shows a multi-modal approach where we improve a state-of-the-art speaker diarization system by combining standard acoustic features (MFCCs) with compressed domain video features. The approach is evaluated on over 4.5 hours...
We address the problem of both estimating the dominant person in a meeting from a single audio source and identifying them visually in a multi-camera setting. We use a speaker diarization algorithm to perform speaker segmentation and clustering, representing when they spoke. Using a greedy ordered audio-visual association algorithm, we investigate using the speaker clusters to find the corresponding...
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