  Domain generalization remains a critical problem for speaker recognition,
even with the state-of-the-art architectures based on deep neural nets. For
example, a model trained on reading speech may largely fail when applied to
scenarios of singing or movie. In this paper, we propose a domain-invariant
projection to improve the generalizability of speaker vectors. This projection
is a simple neural net and is trained following the Model-Agnostic
Meta-Learning (MAML) principle, for which the objective is to classify speakers
in one domain if it had been updated with speech data in another domain. We
tested the proposed method on CNCeleb, a new dataset consisting of
single-speaker multi-condition (SSMC) data. The results demonstrated that the
MAML-based domain-invariant projection can produce more generalizable speaker
vectors, and effectively improve the performance in unseen domains.
