  Speaker verification can be formulated as a representation learning task,
where speaker-discriminative embeddings are extracted from utterances of
variable lengths. Momentum Contrast (MoCo) is a recently proposed unsupervised
representation learning framework, and has shown its effectiveness for learning
good feature representation for downstream vision tasks. In this work, we apply
MoCo to learn speaker embedding from speech segments. We explore MoCo for both
unsupervised learning and pretraining settings. In the unsupervised scenario,
embedding is learned by MoCo from audio data without using any speaker specific
information. On a large scale dataset with $2,500$ speakers, MoCo can achieve
EER $4.275\%$ trained unsupervisedly, and the EER can decrease further to
$3.58\%$ if extra unlabelled data are used. In the pretraining scenario,
encoder trained by MoCo is used to initialize the downstream supervised
training. With finetuning on the MoCo trained model, the equal error rate (EER)
reduces $13.7\%$ relative ($1.44\%$ to $1.242\%$) compared to a carefully tuned
baseline training from scratch. Comparative study confirms the effectiveness of
MoCo learning good speaker embedding.
