1. A method for inter-dataset variability compensation in a speaker recognition system, the method comprising: operating at least one hardware processor of a speaker recognition system to compensate for inter-dataset variability, by: receiving a heterogeneous development dataset comprising: multiple samples embodied as digital files, and digital metadata associated with at least some of the multiple samples; dividing the multiple samples into multiple homogenous subsets, based on the digital metadata; computing a statistical measure of high-level features of each of the multiple homogenous subsets, to produce multiple central high-level features for the multiple homogenous subsets, respectively; computing an inter-dataset variability subspace spanned by the multiple central high-level features; removing the inter-dataset variability subspace from the high-level features of the multiple homogenous subsets, to produce denoised samples; training the speaker recognition system using the denoised samples; and performing speaker recognition using the trained speaker recognition system.