Most state-of-the-art approaches address speaker diariza- tion as a hierarchical agglomerative clustering problem in the audio domain. In this paper, we propose to revisit one of them: speech turns clustering based on the Bayesian Information Cri- terion (a.k.a. BIC clustering). First, we show how to model it as an integer linear programming (ILP) problem. Its resolu- tion leads to the same overall diarization error rate as standard BIC clustering but generates significantly purer speaker clus- ters. Then, we describe how this approach can easily be ex- tended to the audiovisual domain and TV broadcast in particu- lar. The straightforward integration of detected overlaid names (used to introduce guests or journalists, and obtained via video OCR) into a multimodal ILP problem yields significantly better speaker diarization results. Finally, we explain how this novel paradigm can incidentally be used for unsupervised speaker identification (i.e. not relying on any prior acoustic speaker models). Experiments on the REPERE TV broadcast corpus show that it achieves performance close to that of an oracle ca- pable of identifying any speaker as long as their name appears on screen at least once in the video.