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 - 5 second)

test utterances required by the gaming scheme.

PDF file

In  ICASSP

Publisher  IEEE

Details