  In this paper, we propose an effective training strategy to ex-tract robust
speaker representations from a speech signal. Oneof the key challenges in
speaker recognition tasks is to learnlatent representations or embeddings
containing solely speakercharacteristic information in order to be robust in
terms of intra-speaker variations. By modifying the network architecture
togenerate both speaker-related and speaker-unrelated representa-tions, we
exploit a learning criterion which minimizes the mu-tual information between
these disentangled embeddings. Wealso introduce an identity change loss
criterion which utilizes areconstruction error to different utterances spoken
by the samespeaker. Since the proposed criteria reduce the variation ofspeaker
characteristics caused by changes in background envi-ronment or spoken content,
the resulting embeddings of eachspeaker become more consistent. The
effectiveness of the pro-posed method is demonstrated through two tasks;
disentangle-ment performance, and improvement of speaker recognition ac-curacy
compared to the baseline model on a benchmark dataset,VoxCeleb1. Ablation
studies also show the impact of each cri-terion on overall performance.
