  Many approaches can derive information about a single speaker's identity from
the speech by learning to recognize consistent characteristics of acoustic
parameters. However, it is challenging to determine identity information when
there are multiple concurrent speakers in a given signal. In this paper, we
propose a novel deep speaker representation strategy that can reliably extract
multiple speaker identities from an overlapped speech. We design a network that
can extract a high-level embedding that contains information about each
speaker's identity from a given mixture. Unlike conventional approaches that
need reference acoustic features for training, our proposed algorithm only
requires the speaker identity labels of the overlapped speech segments. We
demonstrate the effectiveness and usefulness of our algorithm in a speaker
verification task and a speech separation system conditioned on the target
speaker embeddings obtained through the proposed method.
