Audio-Video Speaker Diarization for Unsupervised Speaker and Face Model Creation

摘要

Our goal is to create speaker models in audio domain and face models in video domain from a set of videos in an unsupervised manner. Such models can be used later for speaker identification in audio domain (answering the question "Who was speaking and when") and/or for face recognition ("Who was seen and when") for given videos that contain speaking persons. The proposed system is based on an audio-video diarization system that tries to resolve the disadvantages of the individual modalities. Experiments on broadcasts of Czech parliament meetings show that the proposed combination of individual audio and video diarization systems yields an improvement of the diarization error rate (DER).