In this paper, we conduct a cross-dataset study on parametric and non-parametric raw-waveform based speaker embeddings through speaker verification experiments. In general, we observe a more significant performance degradation of these raw-waveform systems compared to spectral based systems. We then propose two strategies to improve the performance of raw-waveform based systems on cross-dataset tests. The first strategy is to change the real-valued filters into analytic filters to ensure shift-invariance. The second strategy is to apply variational dropout to non-parametric filters to prevent them from overfitting irrelevant nuance features.
@article{arxiv.2110.04265,
title = {A study of the robustness of raw waveform based speaker embeddings under mismatched conditions},
author = {Ge Zhu and Frank Cwitkowitz and Zhiyao Duan},
journal= {arXiv preprint arXiv:2110.04265},
year = {2021}
}