Graph-based Label Propagation for Semi-Supervised Speaker Identification

Abstract

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.

arXiv commentTo appear in Interspeech 2021

References in corpus (6)

• VoxCeleb2: Deep Speaker Recognition
• VoxCeleb: a large-scale speaker identification dataset
• Simplifying Graph Convolutional Networks
• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
• Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition
• Speaker attribution with voice profiles by graph-based semi-supervised learning

Cited by in corpus (2)

• Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets
• Improving Text-Independent Speaker Verification with Auxiliary Speakers Using Graph