  Household speaker identification with few enrollment utterances is an
important yet challenging problem, especially when household members share
similar voice characteristics and room acoustics. A common embedding space
learned from a large number of speakers is not universally applicable for the
optimal identification of every speaker in a household. In this work, we first
formulate household speaker identification as a few-shot open-set recognition
task and then propose a novel embedding adaptation framework to adapt speaker
representations from the given universal embedding space to a
household-specific embedding space using a set-to-set function, yielding better
household speaker identification performance. With our algorithm, Open-set
Few-shot Embedding Adaptation with Transformer (openFEAT), we observe that the
speaker identification equal error rate (IEER) on simulated households with 2
to 7 hard-to-discriminate speakers is reduced by 23% to 31% relative.
