  This paper proposes a unified deep speaker embedding framework for modeling
speech data with different sampling rates. Considering the narrowband
spectrogram as a sub-image of the wideband spectrogram, we tackle the joint
modeling problem of the mixed-bandwidth data in an image classification manner.
From this perspective, we elaborate several mixed-bandwidth joint training
strategies under different training and test data scenarios. The proposed
systems are able to flexibly handle the mixed-bandwidth speech data in a single
speaker embedding model without any additional downsampling, upsampling,
bandwidth extension, or padding operations. We conduct extensive experimental
studies on the VoxCeleb1 dataset. Furthermore, the effectiveness of the
proposed approach is validated by the SITW and NIST SRE 2016 datasets.
