  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.
