Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words
This work addresses robustness issues in wake-up word detection systems for real-world applications, though it appears incremental as it builds on existing adversarial sample generation techniques.
The paper tackles the problem of wake-up word detection models degrading when encountering adversarial samples from confusing words, and proposes methods to generate such adversarial samples for training, resulting in improved robustness in both common and confusing word scenarios.
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.