  The universal speech attributes for x-vector based speaker verification (SV)
are addressed in this paper. The manner and place of articulation form the
fundamental speech attribute unit (SAU), and then new speech attribute (NSA)
units for acoustic modeling are generated by tied tri-SAU states. An improved
cross-stitch network is adopted as a multitask learning (MTL) framework for
integrating these universal speech attributes into the x-vector network
training process. Experiments are conducted on common condition 5 (CC5) of the
core-core and the 10 s-10 s tests of the NIST SRE10 evaluation set, and the
proposed algorithm can achieve consistent improvements over the baseline
x-vector on both these tasks.
