  Deep speaker embeddings have shown promising results in speaker recognition,
as well as in other speaker-related tasks. However, some issues are still under
explored, for instance, the information encoded in these representations and
their influence on downstream tasks. Four deep speaker embeddings are studied
in this paper, namely, d-vector, x-vector, ResNetSE-34 and ECAPA-TDNN. Inspired
by human voice mechanisms, we explored possibly encoded information from
perspectives of identity, contents and channels; Based on this, experiments
were conducted on three categories of speaker-related tasks to further explore
impacts of different deep embeddings, including discriminative tasks (speaker
verification and diarization), guiding tasks (target speaker detection and
extraction) and regulating tasks (multi-speaker text-to-speech). Results show
that all deep embeddings encoded channel and content information in addition to
speaker identity, but the extent could vary and their performance on
speaker-related tasks can be tremendously different: ECAPA-TDNN is dominant in
discriminative tasks, and d-vector leads the guiding tasks, while regulating
task is less sensitive to the choice of speaker representations. These may
benefit future research utilizing speaker embeddings.
