  State-of-the-art text-independent speaker verification systems typically use
cepstral features or filter bank energies as speech features. Recent studies
attempted to extract speaker embeddings directly from raw waveforms and have
shown competitive results. In this paper, we propose a novel multi-scale
waveform encoder that uses three convolution branches with different time
scales to compute speech features from the waveform. These features are then
processed by squeeze-and-excitation blocks, a multi-level feature aggregator,
and a time delayed neural network (TDNN) to compute speaker embedding. We show
that the proposed embeddings outperform existing raw-waveform-based speaker
embeddings on speaker verification by a large margin. A further analysis of the
learned filters shows that the multi-scale encoder attends to different
frequency bands at its different scales while resulting in a more flat overall
frequency response than any of the single-scale counterparts.
