Speaker identification is a well-established research problem but has not been a major application used in gaming scenarios. In this paper, we propose a new algorithm for the open-set, text-independent, speaker ID problem, applied as an important component (among other cues) of a game player identification system. This scenario poses new challenges: far-field, limited training and very short test data, and almost real-time processing. To tackle this, we introduce new and more informative feature sets. The scores given by these feature sets are then combined in an optimal way to construct the final score. Experimental results on the gaming device’s processed reverberated-speech show the effectiveness of the new features, and that reliable decisions can be made after very short (2 Р5 second) test utterances required by the gaming scheme.