  Speaker identification typically involves three stages. First, a front-end
speaker embedding model is trained to embed utterance and speaker profiles.
Second, a scoring function is applied between a runtime utterance and each
speaker profile. Finally, the speaker is identified using nearest neighbor
according to the scoring metric. To better distinguish speakers sharing a
device within the same household, we propose a household-adapted nonlinear
mapping to a low dimensional space to complement the global scoring metric. The
combined scoring function is optimized on labeled or pseudo-labeled speaker
utterances. With input dropout, the proposed scoring model reduces EER by
45-71% in simulated households with 2 to 7 hard-to-discriminate speakers per
household. On real-world internal data, the EER reduction is 49.2%. From t-SNE
visualization, we also show that clusters formed by household-adapted speaker
embeddings are more compact and uniformly distributed, compared to clusters
formed by global embeddings before adaptation.
