ISCA Archive Interspeech 2020
ISCA Archive Interspeech 2020

Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification

Xiaoyang Qu, Jianzong Wang, Jing Xiao

State-of-the-art speaker verification models are based on deep learning techniques, which heavily depend on the hand-designed neural architectures from experts or engineers. We borrow the idea of neural architecture search (NAS) for the text-independent speaker verification task. As NAS can learn deep network structures automatically, we introduce the NAS conception into the well-known x-vector network. Furthermore, this paper proposes an evolutionary algorithm enhanced neural architecture search method called Auto-Vector to automatically discover promising networks for the speaker verification task. The experimental results demonstrate our NAS-based model outperforms state-of-the-art speaker verification models.


doi: 10.21437/Interspeech.2020-3057

Cite as: Qu, X., Wang, J., Xiao, J. (2020) Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification. Proc. Interspeech 2020, 961-965, doi: 10.21437/Interspeech.2020-3057

@inproceedings{qu20_interspeech,
  author={Xiaoyang Qu and Jianzong Wang and Jing Xiao},
  title={{Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={961--965},
  doi={10.21437/Interspeech.2020-3057},
  issn={2958-1796}
}