  In this paper, we propose a Boosting Tail Neural Network (BTNN) for improving
the performance of Realtime Custom Keyword Spotting (RCKS) that is still an
industrial challenge for demanding powerful classification ability with limited
computation resources. Inspired by Brain Science that a brain is only partly
activated for a nerve simulation and numerous machine learning algorithms are
developed to use a batch of weak classifiers to resolve arduous problems, which
are often proved to be effective. We show that this method is helpful to the
RCKS problem. The proposed approach achieve better performances in terms of
wakeup rate and false alarm.
  In our experiments compared with those traditional algorithms that use only
one strong classifier, it gets 18\% relative improvement. We also point out
that this approach may be promising in future ASR exploration.
