Detecting speaking intentions in multi-user VR environments can facilitate turn-taking, thereby making group interactions more effective in VR environments. This study aims to establish the recognition of speaking intentions based on motion and gaze data from VR devices during interactions involving multiple participants. Through in-depth statistical data analysis, we identified head and right-hand features associated with speaking intentions and discovered different temporal dependencies for motion and gaze features. Using these features, we show that these features allow for effective detection of speaking intentions, with the random forest (RF) classifier achieving the highest F1 score of 0.824 by mixing motion and gaze features with different data window sizes.