Speaker identification in the household scenario (e.g., for smart speakers)
is typically based on only a few enrollment utterances but a much larger set of
unlabeled data, suggesting semisupervised learning to improve speaker profiles.
We propose a graph-based semi-supervised learning approach for speaker
identification in the household scenario, to leverage the unlabeled speech
samples. In contrast to most of the works in speaker recognition that focus on
speaker-discriminative embeddings, this work focuses on speaker label inference
(scoring). Given a pre-trained embedding extractor, graph-based learning allows
us to integrate information about both labeled and unlabeled utterances.
Considering each utterance as a graph node, we represent pairwise utterance
similarity scores as edge weights. Graphs are constructed per household, and
speaker identities are propagated to unlabeled nodes to optimize a global
consistency criterion. We show in experiments on the VoxCeleb dataset that this
approach makes effective use of unlabeled data and improves speaker
identification accuracy compared to two state-of-the-art scoring methods as
well as their semi-supervised variants based on pseudo-labels.

本文提出了一种基于图形的半监督学习方法，用于家庭情景下的说话人识别，以利用未标记的语音样本，并在 VoxCeleb 数据集上的实验中显示出，与两种最先进的评分方法以及基于伪标签的半监督变量相比，该方法有效地利用了未标记的数据并提高了说话人识别的准确性。