  Wake-up word detection models are widely used in real life, but suffer from
severe performance degradation when encountering adversarial samples. In this
paper we discuss the concept of confusing words in adversarial samples.
Confusing words are commonly encountered, which are various kinds of words that
sound similar to the predefined keywords. To enhance the wake word detection
system's robustness against confusing words, we propose several methods to
generate the adversarial confusing samples for simulating real confusing words
scenarios in which we usually do not have any real confusing samples in the
training set. The generated samples include concatenated audio, synthesized
data, and partially masked keywords. Moreover, we use a domain embedding
concatenated system to improve the performance. Experimental results show that
the adversarial samples generated in our approach help improve the system's
robustness in both the common scenario and the confusing words scenario. In
addition, we release the confusing words testing database called HI-MIA-CW for
future research.
