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Many approaches can derive information about a single speaker's identity fromthe speech by learning to recognize consistent characteristics of acousticparameters. However, it is challenging to determine identity information whenthere are multiple concurrent speakers in a given signal. In this paper, wepropose a novel deep speaker representation strategy that can reliably extractmultiple speaker identities from an overlapped speech. We design a network thatcan extract a high-level embedding that contains information about eachspeaker's identity from a given mixture. Unlike conventional approaches thatneed reference acoustic features for training, our proposed algorithm onlyrequires the speaker identity labels of the overlapped speech segments. Wedemonstrate the effectiveness and usefulness of our algorithm in a speakerverification task and a speech separation system conditioned on the targetspeaker embeddings obtained through the proposed method.

MIRNet: Learning multiple identities representations in overlapped speech