Speaker identification (SID) in the household scenario (e.g., for smart
speakers) is an important but challenging problem due to limited number of
labeled (enrollment) utterances, confusable voices, and demographic imbalances.
Conventional speaker recognition systems generalize from a large random sample
of speakers, causing the recognition to underperform for households drawn from
specific cohorts or otherwise exhibiting high confusability. In this work, we
propose a graph-based semi-supervised learning approach to improve
household-level SID accuracy and robustness with locally adapted graph
normalization and multi-signal fusion with multi-view graphs. Unlike other work
on household SID, fairness, and signal fusion, this work focuses on speaker
label inference (scoring) and provides a simple solution to realize
household-specific adaptation and multi-signal fusion without tuning the
embeddings or training a fusion network. Experiments on the VoxCeleb dataset
demonstrate that our approach consistently improves the performance across
households with different customer cohorts and degrees of confusability.

本文提出了基于图的半监督学习方法，通过本地适应的图归一化和多信号融合实现家庭中的说话人识别，实验表明该方法可有效应对标注数据少、声音混杂等问题。