Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets
Abstract
Speaker identification typically involves three stages. First, a front-end speaker embedding model is trained to embed utterance and speaker profiles. Second, a scoring function is applied between a runtime utterance and each speaker profile. Finally, the speaker is identified using nearest neighbor according to the scoring metric. To better distinguish speakers sharing a device within the same household, we propose a household-adapted nonlinear mapping to a low dimensional space to complement the global scoring metric. The combined scoring function is optimized on labeled or pseudo-labeled speaker utterances. With input dropout, the proposed scoring model reduces EER by 45-71% in simulated households with 2 to 7 hard-to-discriminate speakers per household. On real-world internal data, the EER reduction is 49.2%. From t-SNE visualization, we also show that clusters formed by household-adapted speaker embeddings are more compact and uniformly distributed, compared to clusters formed by global embeddings before adaptation.
Cite
@article{arxiv.2109.02576,
title = {Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets},
author = {Zhenning Tan and Yuguang Yang and Eunjung Han and Andreas Stolcke},
journal= {arXiv preprint arXiv:2109.02576},
year = {2022}
}
Comments
Submitted to ASRU 2021