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The recent development of the i-vector framework for speaker recognition has set a new performance standard in the research field. An i-vector is a compact representation of a speakers utterance extracted from a total variability subspace. Prior to classification using a cosine kernel, i-vectors are projected into an linear discriminant analysis (LDA) space in order to reduce inter-session variability and enhance speaker discrimination. The accurate estimation of this LDA space from a training dataset is crucial to detection performance. A typical training dataset, however, does not consist of utterances acquired through all sources of interest for each speaker. This has the effect of introducing systematic variation related to the speech source in the between-speaker covariance matrix and results in an incomplete representation of the within-speaker scatter matrix used for LDA. The recently proposed source-normalized …