Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets

Zhenning Tan; Yuguang Yang; Eunjung Han; Andreas Stolcke

doi:10.1109/asru51503.2021.9687975

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arXiv · 2109.02576

Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets

Zhenning Tan·Yuguang Yang·Eunjung Han·Andreas Stolcke

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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.

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BibTeX RIS

Zhenning Tan, Yuguang Yang, Eunjung Han, Andreas Stolcke. 2021-09-06. Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets. https://doi.org/10.1109/asru51503.2021.9687975

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