Speaker recognition by location in the space of reference speakers

Abstract

Speaker representation by location in a reference space is a new technique of speaker recognition and adaptation. It consists in representing a speaker relatively rather than absolutely, by comparing him to a set of well-trained speakers. The main motivation is to obtain a compact modeling of every speaker, which gives similar performances to those of the state of the art GMM-UBM. Thus, instead of estimating numerous parameters of an absolute model of the speaker, only a few parameters of a model relatively to other speaker models called reference speakers are estimated. In this study, several points are addressed that are related to the concept of relative location in speaker recognition. Firstly, the reference speaker space is built. Then the appropriate metrics in this space are investigated in order to perform speaker recognition in a geometrical approach. Finally, a statistical approach for speaker location is used to eliminate the weaknesses of the geometrical approach. In-depth evaluations on a telephone database show that the concept of relative location is a promising technique for speaker verification. Therefore, it can be concluded that the most important motivation for using anchor models is their computational efficiency for indexing tasks.