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.
@inproceedings{Qu2020, 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}, url={http://dx.doi.org/10.21437/Interspeech.2020-3057} }