Dealing with additive noise in speaker recognition systems based on i-vector approach

In the last years, the i-vector approach became the state-of-the-art in speaker recognition systems. As in previous approaches, i-vector -based systems suffer greatly in presence of additive noise, especially in low SNR cases. In this paper, we will describe a statistical framework allowing to estimate a clean i-vector given the noisy one or to integrate, directly, statistical knowledges about the noise and clean i-vectors in the scoring phase. The proposed procedure is essentially based on a method which enables to produce statistical knowledge about the noise effect in the i-vector domain. The work presented here is based on the hypothesis that the noise effect is Gaussian and additive in the i-vector space. To validate our approach, experiments were carried out on NIST 2008 data (det7). Significant improvement was observed compared to the baseline system and to the "muti-style" backend training technique.