1. A method for inter-dataset variability compensation, the method comprising using at least one hardware processor for: receiving a heterogeneous development dataset comprising multiple samples and metadata associated with at least some of the multiple samples; dividing the multiple samples into multiple homogenous subsets, based on the 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; and training a machine learning system using the denoised speech samples.