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arXiv · 2309.03019

Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation

Danwei Cai·Ming Li
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Abstract

This paper explores the use of ASR-pretrained Conformers for speaker verification, leveraging their strengths in modeling speech signals. We introduce three strategies: (1) Transfer learning to initialize the speaker embedding network, improving generalization and reducing overfitting. (2) Knowledge distillation to train a more flexible speaker verification model, incorporating frame-level ASR loss as an auxiliary task. (3) A lightweight speaker adaptor for efficient feature conversion without altering the original ASR Conformer, allowing parallel ASR and speaker verification. Experiments on VoxCeleb show significant improvements: transfer learning yields a 0.48% EER, knowledge distillation results in a 0.43% EER, and the speaker adaptor approach, with just an added 4.92M parameters to a 130.94M-parameter model, achieves a 0.57% EER. Overall, our methods effectively transfer ASR capabilities to speaker verification tasks.

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BibTeXRIS

Danwei Cai, Ming Li. 2023-09-06. Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation. https://doi.org/10.1109/taslp.2024.3419426

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