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.

翻译：在本文中,我们进行了一项关于通过语音校验实验嵌入的参数和非参数原始波形扬声器的交叉数据集研究;一般而言,我们观察到这些原始波形系统与光谱系统相比的性能下降幅度更大;然后,我们提出了两项战略,以改进以交叉数据集测试为基础的原始波形系统的性能;第一项战略是将实际价值的过滤器改为分析过滤器,以确保不动。第二项战略是将变式退出应用到非参数过滤器,以防止它们过度配置不相干微分特征。