Towards Audio-Visual On-line Diarization Of Participants In Group Meetings
We propose a fully automated, unsupervised, and non-int\-rusive method of identifying the current speaker audio-vis\-ually in a group conversation. This is achieved without specialized hardware, user interaction, or prior assignment of microphones to participants. Speakers are identified acoustically using a novel on-line speaker diarization approach. The output is then used to find the corresponding person in a four-camera video stream by approximating individual activity with computationally efficient features. We present results showing the robustness of the association on over 4.5 hours of non-scripted audio-visual meeting data.
Record created on 2010-02-11, modified on 2016-08-08