Handling i-vectors from different recording conditions using multi-channel simplified PLDA in speaker recognition

In this work, we address the problem of having i-vectors that have been produced in different channel conditions. Traditionally, this problem has been handled training the LDA covariance matrices pooling the data of all the conditions or averaging the covariance matrices of each condition in different ways. We present a PLDA variant that we call, multi-channel SPLDA, where the speaker space distribution is common to all i-vectors and the channel space distribution depends on the type of channel where the segment has been recorded. We test our approach on the telephone part of the NIST SRE10 extended condition where we added some additive noises to the test segments. We compare results of a SPLDA model trained only with clean data, SPLDA trained with pooled noisy and clean data and our MCSPLDA model.