Iterative Bayesian and MMSE-based noise compensation techniques for speaker recognition in the i-vector space

Dealing with additive noise in the i-vector space can be challenging due to the complexity of its effect in that space. Several compensation techniques have been proposed in the last years to either remove the noise effect by setting a noise model in the i-vector space or build better scoring techniques that take environment perturbations into account. We recently presented a new efficient Bayesian cleaning technique operating in the ivector domain named I-MAP that improves the baseline system performance by up to 60%. This technique is based on Gaussian models for the clean and noise i-vectors distributions. After IMAP transformation, these hypothesis are probably less correct. For this reason, we propose to apply another MMSE-based approach that uses the Kabsch algorithm. For a certain noise, it estimates the best translation vector and rotation matrix between a set of train noisy i-vectors and their clean counterparts based on RMSD criterion. This transformation is then applied on noisy test i-vectors in order to remove the noise effect. We show that applying the Kabsch algorithm allows to reach a 40% relative improvement in EER(%) compared to a baseline system performance and that, when combined with I-MAP and repeated iteratively, it allows to reach 85% of relative improvement. keywords: i-vector, additive noise, Kabsch algorithm, IMAP