  For text-independent short-utterance speaker recognition (SUSR), the
performance often degrades dramatically. This paper presents a combination
approach to the SUSR tasks with two phonetic-aware systems: one is the
DNN-based i-vector system and the other is our recently proposed
subregion-based GMM-UBM system. The former employs phone posteriors to
construct an i-vector model in which the shared statistics offers stronger
robustness against limited test data, while the latter establishes a
phone-dependent GMM-UBM system which represents speaker characteristics with
more details. A score-level fusion is implemented to integrate the respective
advantages from the two systems. Experimental results show that for the
text-independent SUSR task, both the DNN-based i-vector system and the
subregion-based GMM-UBM system outperform their respective baselines, and the
score-level system combination delivers performance improvement.
