  This paper introduces the system submitted by the DKU-SMIIP team for the
Auto-KWS 2021 Challenge. Our implementation consists of a two-stage keyword
spotting system based on query-by-example spoken term detection and a speaker
verification system. We employ two different detection algorithms in our
proposed keyword spotting system. The first stage adopts subsequence dynamic
time warping for template matching based on frame-level language-independent
bottleneck feature and phoneme posterior probability. We use a sliding window
template matching algorithm based on acoustic word embeddings to further verify
the detection from the first stage. As a result, our KWS system achieves an
average score of 0.61 on the feedback dataset, which outperforms the baseline1
system by 0.25.
