Keyword spotting (KWS) system constitutes a critical component
of human-computer interfaces, which detects the specific keyword
from a continuous stream of audio. The goal of KWS is providing a
high detection accuracy at a low false alarm rate while having small
memory and computation requirements. The DNN-based KWS system
faces a large class imbalance during training because the amount
of data available for the keyword is usually much less than the background
speech, which overwhelms training and leads to a degenerate
model. In this paper, we explore the focal loss for the training of a
small-footprint KWS system. It can automatically down-weight the
contribution of easy samples during training and focus the model on
hard samples, which naturally solves the class imbalance and allows
us to efficiently utilize all data available. Furthermore, many keywords
of Chinese conversational assistants are repeated words due
to the idiomatic usage, such as ‘XIAO DU XIAO DU’. We propose
a double-edge-triggered detecting method for the repeated keyword,
which significantly reduces the false alarm rate relative to the single
threshold method. Systematic experiments demonstrate significant
further improvements compared to the baseline system.