RealVAD: A Real-world Dataset for Voice Activity Detection The task of automatically detecting “Who is Speaking and When” is broadly named as Voice Activity Detection (VAD). Automatic VAD is a very important task and also the foundation of several domains, e.g., human-human, human-computer/ robot/ virtual-agent interaction analyses, and industrial applications. RealVAD dataset is constructed from a YouTube video composed of a panel discussion lasting approx. 83 minutes. The audio is available from a single channel. There is one static camera capturing all panelists, the moderator and audiences. Particular aspects of RealVAD dataset are: It is composed of panelists with different nationalities (British, Dutch, French, German, Italian, American, Mexican, Columbian, Thai). This aspect allows studying the effect of ethnic origin variety to the automatic VAD. There is a gender balance such that there are four female and five male panelists. The panelists are sitting in two rows and they can be gazing audience, other panelists, their laptop, the moderator or anywhere in the room while speaking or not-speaking. Therefore, they were captured not only from frontal-view but also from side-view varying based on their instant posture and head orientation. The panelists are moving freely and are doing various spontaneous actions (e.g., drinking water, checking their cell phone, using their laptop, etc.), resulting in different postures. The panelists’ body parts are sometimes partially occluded by their/other's body part or belongings (e.g., laptop). There are also natural changes of illumination and shadow rising on the wall behind the panelists in the back row. Especially, for the panelists sitting in the front row, there is sometimes background motion occurring when the person(s) behind them moves. The annotations includes: The upper body detection of nine panelists in bounding box form. Associated VAD ground-truth (speaking, not-speaking) for nine panelists. Acoustic features extracted from the video: MFCC and raw filterbank energies. All info regarding the annotations are given in the ReadMe.txt and Acoustic Features README.txt files. When using this dataset for your research, please cite the following paper in your publication: C. Beyan, M. Shahid and V. Murino, "RealVAD: A Real-world Dataset and A Method for Voice Activity Detection by Body Motion Analysis", in IEEE Transactions on Multimedia, 2020.