  The performance of automatic speaker recognition systems degrades when facing
distorted speech data containing additive noise and/or reverberation.
Statistical uncertainty propagation has been introduced as a promising paradigm
to address this challenge. So far, different uncertainty propagation methods
have been proposed to compensate noise and reverberation in i-vectors in the
context of speaker recognition. They have achieved promising results on small
datasets such as YOHO and Wall Street Journal, but little or no improvement on
the larger, highly variable NIST Speaker Recognition Evaluation (SRE) corpus.
In this paper, we propose a complete uncertainty propagation method, whereby we
model the effect of uncertainty both in the computation of unbiased Baum-Welch
statistics and in the derivation of the posterior expectation of the i-vector.
We conduct experiments on the NIST-SRE corpus mixed with real domestic noise
and reverberation from the CHiME-2 corpus and preprocessed by multichannel
speech enhancement. The proposed method improves the equal error rate (EER) by
4% relative compared to a conventional i-vector based speaker verification
baseline. This is to be compared with previous methods which degrade
performance.
