  We introduce a monaural neural speaker embeddings extractor that computes an
embedding for each speaker present in a speech mixture. To allow for supervised
training, a teacher-student approach is employed: the teacher computes the
target embeddings from each speaker's utterance before the utterances are added
to form the mixture, and the student embedding extractor is then tasked to
reproduce those embeddings from the speech mixture at its input. The system
much more reliably verifies the presence or absence of a given speaker in a
mixture than a conventional speaker embedding extractor, and even exhibits
comparable performance to a multi-channel approach that exploits spatial
information for embedding extraction. Further, it is shown that a speaker
embedding computed from a mixture can be used to check for the presence of that
speaker in another mixture.
