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。

基于图的多视角融合和本地适应：缓解家庭内说话人认证的混淆问题

Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification

Training deep graph neural networks (GNNs) is notoriously hard. Besides the
standard plights in training deep architectures such as vanishing gradients and
overfitting, it also uniquely suffers from over-smoothing, information
squashing, and so on, which limits their potential power for encoding the
high-order neighbor structure in large-scale graphs. Although numerous efforts
are proposed to address these limitations, such as various forms of skip
connections, graph normalization, and random dropping, it is difficult to
disentangle the advantages brought by a deep GNN architecture from those
"tricks" necessary to train such an architecture. Moreover, the lack of a
standardized benchmark with fair and consistent experimental settings poses an
almost insurmountable obstacle to gauge the effectiveness of new mechanisms. In
view of those, we present the first fair and reproducible benchmark dedicated
to assessing the "tricks" of training deep GNNs. We categorize existing
approaches, investigate their hyperparameter sensitivity, and unify the basic
configuration. Comprehensive evaluations are then conducted on tens of
representative graph datasets including the recent large-scale Open Graph
Benchmark, with diverse deep GNN backbones. We demonstrate that an organic
combo of initial connection, identity mapping, group and batch normalization
attains the new state-of-the-art results for deep GNNs on large datasets. Codes
are available: this https URL

本研究提供了第一个公平、可重复的基准测试，重点研究了训练深度图神经网络中的技巧，并利用该测试在数十个代表性图数据集上进行了全面评估，证明了初始连接、标识映射、分组和批量归一化的有机组合实现了深度 GNN 在大型数据集上的最新成果。