Attentive Adversarial Learning for Domain-invariant Training

@article{Meng2019AttentiveAL,
  title={Attentive Adversarial Learning for Domain-invariant Training},
  author={Zhong Meng and J. Li and Y. Gong},
  journal={ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2019},
  pages={6740-6744}
}
  • Zhong Meng, J. Li, Y. Gong
  • Published 2019
  • Computer Science, Engineering, Mathematics
  • ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we… Expand
Adversarial Speaker Adaptation
  • Zhong Meng, J. Li, Y. Gong
  • Computer Science, Engineering
  • ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019
Speaker Adaptation for Attention-Based End-to-End Speech Recognition
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition
Semi-Supervised end-to-end Speech Recognition via Local Prior Matching
Semi-Supervised Speech Recognition via Local Prior Matching
Conditional Teacher-student Learning
  • Zhong Meng, J. Li, Y. Zhao, Y. Gong
  • Computer Science, Engineering
  • ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019

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  • ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019
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