ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

ICSpk: Interpretable Complex Speaker Embedding Extractor from Raw Waveform

Junyi Peng, Xiaoyang Qu, Jianzong Wang, Rongzhi Gu, Jing Xiao, Lukáš Burget, Jan Černocký

Recently, extracting speaker embedding directly from raw waveform has drawn increasing attention in the field of speaker verification. Parametric real-valued filters in the first convolutional layer are learned to transform the waveform into time-frequency representations. However, these methods only focus on the magnitude spectrum and the poor interpretability of the learned filters limits the performance. In this paper, we propose a complex speaker embedding extractor, named ICSpk, with higher interpretability and fewer parameters. Specifically, at first, to quantify the speaker-related frequency response of waveform, we modify the original short-term Fourier transform filters into a family of complex exponential filters, named interpretable complex (IC) filters. Each IC filter is confined by a complex exponential filter parameterized by frequency. Then, a deep complex-valued speaker embedding extractor is designed to operate on the complex-valued output of IC filters. The proposed ICSpk is evaluated on VoxCeleb and CNCeleb databases. Experimental results demonstrate the IC filters-based system exhibits a significant improvement over the complex spectrogram based systems. Furthermore, the proposed ICSpk outperforms existing raw waveform based systems by a large margin.

doi: 10.21437/Interspeech.2021-2016

Cite as: Peng, J., Qu, X., Wang, J., Gu, R., Xiao, J., Burget, L., Černocký, J. (2021) ICSpk: Interpretable Complex Speaker Embedding Extractor from Raw Waveform. Proc. Interspeech 2021, 511-515, doi: 10.21437/Interspeech.2021-2016

  author={Junyi Peng and Xiaoyang Qu and Jianzong Wang and Rongzhi Gu and Jing Xiao and Lukáš Burget and Jan Černocký},
  title={{ICSpk: Interpretable Complex Speaker Embedding Extractor from Raw Waveform}},
  booktitle={Proc. Interspeech 2021},