  Speaker recognition, recognizing speaker identities based on voice alone,
enables important downstream applications, such as personalization and
authentication. Learning speaker representations, in the context of supervised
learning, heavily depends on both clean and sufficient labeled data, which is
always difficult to acquire. Noisy unlabeled data, on the other hand, also
provides valuable information that can be exploited using self-supervised
training methods. In this work, we investigate how to pretrain speaker
recognition models by leveraging dialogues between customers and smart-speaker
devices. However, the supervisory information in such dialogues is inherently
noisy, as multiple speakers may speak to a device in the course of the same
dialogue. To address this issue, we propose an effective rejection mechanism
that selectively learns from dialogues based on their acoustic homogeneity.
Both reconstruction-based and contrastive-learning-based self-supervised
methods are compared. Experiments demonstrate that the proposed method provides
significant performance improvements, superior to earlier work. Dialogue
pretraining when combined with the rejection mechanism yields 27.10% equal
error rate (EER) reduction in speaker recognition, compared to a model without
self-supervised pretraining.
