  We propose to learn surrogate functions of universal speech priors for
determined blind speech separation. Deep speech priors are highly desirable due
to their high modelling power, but are not compatible with state-of-the-art
independent vector analysis based on majorization-minimization (AuxIVA), since
deriving the required surrogate function is not easy, nor always possible.
Instead, we do away with exact majorization and directly approximate the
surrogate. Taking advantage of iterative source steering (ISS) updates, we back
propagate the permutation invariant separation loss through multiple iterations
of AuxIVA. ISS lends itself well to this task due to its lower complexity and
lack of matrix inversion. Experiments show large improvements in terms of scale
invariant signal-to-distortion (SDR) ratio and word error rate compared to
baseline methods. Training is done on two speakers mixtures and we experiment
with two losses, SDR and coherence. We find that the learnt approximate
surrogate generalizes well on mixtures of three and four speakers without any
modification. We also demonstrate generalization to a different variation of
the AuxIVA update equations. The SDR loss leads to fastest convergence in
iterations, while coherence leads to the lowest word error rate (WER). We
obtain as much as 36 % reduction in WER.
