  Speech is easily leaked imperceptibly, such as being recorded by mobile
phones in different situations. Private content in speech may be maliciously
extracted through speech enhancement technology. Speech enhancement technology
has developed rapidly along with deep neural networks (DNNs), but adversarial
examples can cause DNNs to fail. In this work, we propose an adversarial method
to degrade speech enhancement systems. Experimental results show that generated
adversarial examples can erase most content information in original examples or
replace it with target speech content through speech enhancement. The word
error rate (WER) between an enhanced original example and enhanced adversarial
example recognition result can reach 89.0%. WER of target attack between
enhanced adversarial example and target example is low to 33.75% . Adversarial
perturbation can bring the rate of change to the original example to more than
1.4430. This work can prevent the malicious extraction of speech.
