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.