  In this paper, we propose a speaker-verification system based on maximum
likelihood linear regression (MLLR) super-vectors, for which speakers are
characterized by m-vectors. These vectors are obtained by a uniform
segmentation of the speaker MLLR super-vector using an overlapped sliding
window. We consider three approaches for MLLR transformation, based on the
conventional $1$-best automatic transcription, on the lattice word
transcription, or on a simple global universal background model (UBM). Session
variability compensation is performed in a post-processing module with
probabilistic linear discriminant analysis (PLDA) or the eigen factor radial
(EFR). Alternatively, we propose a cascade post-processing for the MLLR
super-vector based speaker-verification system.
  In this case, the m-vectors or MLLR super-vectors are first projected onto a
lower-dimensional vector space generated by linear discriminant analysis (LDA).
Next, PLDA session variability compensation and scoring is applied to the
reduced-dimensional vectors. This approach combines the advantages of both
techniques and makes the estimation of PLDA parameters easier. Experimental
results on telephone conversations of the NIST 2008 and 2010 speaker
recognition evaluation (SRE) indicate that the proposed m-vector system
performs significantly better than the conventional system based on the full
MLLR super-vectors. Cascade post-processing further reduces the error rate in
all cases. Finally, we present the results of fusion with a standard i-vector
system in the feature, as well as in the score domain, demonstrating that the
m-vector system is both competitive and complementary with it.
