• Corpus ID: 245650705

Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words

  title={Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words},
  author={Haoxu Wang and Yan Jia and Zeqing Zhao and Xuyang Wang and Junjie Wang and Ming Li},
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… 

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HI-MIA: A Far-Field Text-Dependent Speaker Verification Database and the Baselines

  • Xiaoyi QinHui BuMing Li
  • Computer Science
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2020
A far-field text-dependent speaker verification database named HI-MIA is presented and a set of end-to-end neural network based baseline systems that adopt single-channel data for training are proposed.