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Factor Analysis (FA) based techniques have become the state of the art in automatic speaker verification thanks to their great ability to model session variability. This ability, in turn, relies on accurately estimating a session variability subspace for the operating conditions of interest. In cases such as forensic speaker recognition, however, this requirement cannot always be satisfied due to the very limited quantity of appropriate development data. As a first step toward understanding the application of FA in these restricted data scenarios, this work analyzes the performance of FA with very limited development data and then explores several FA estimation methods that augment the target domain data with examples from a data-rich domain.
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