  Keyword spotting (KWS) and speaker verification (SV) are two important tasks
in speech applications. Research shows that the state-of-art KWS and SV models
are trained independently using different datasets since they expect to learn
distinctive acoustic features. However, humans can distinguish language content
and the speaker identity simultaneously. Motivated by this, we believe it is
important to explore a method that can effectively extract common features
while decoupling task-specific features. Bearing this in mind, a two-branch
deep network (KWS branch and SV branch) with the same network structure is
developed and a novel decoupling feature learning method is proposed to push up
the performance of KWS and SV simultaneously where speaker-invariant keyword
representations and keyword-invariant speaker representations are expected
respectively. Experiments are conducted on Google Speech Commands Dataset
(GSCD). The results demonstrate that the orthogonality regularization helps the
network to achieve SOTA EER of 1.31% and 1.87% on KWS and SV, respectively.
