The absence of some forms of non-verbal communication in virtual reality (VR) can make VR-based group discussions difficult even when a leader is assigned to each group to facilitate discussions. In this paper, we discuss if the sensor data from off-the-shelf VR devices can be used to detect and analyze speaking intention in VR-based group discussions by using personalized and generalized models. Our extensive analysis of experimental data reveals some factors that enable effective feedback to the leader. Our results show the benefits of combining sensor features from leaders and low-engagement participants, and the usefulness of HMD sensor features.