# Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters

arXiv: https://arxiv.org/abs/2210.16032 · HTML: https://mlsanity.com/paper/2210.16032/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2022.

## Built on by (most cited first, 9 of 9)

- [ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models](https://mlsanity.com/paper/2401.17230/) (2024)
- [MultiWay-Adapater: Adapting large-scale multi-modal models for scalable image-text retrieval](https://mlsanity.com/paper/2309.01516/) (2023)
- [Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation](https://mlsanity.com/paper/2309.03019/) (2023)
- [Interface Design for Self-Supervised Speech Models](https://mlsanity.com/paper/2406.12209/) (2024)
- [Self-supervised Speech Models for Word-Level Stuttered Speech Detection](https://mlsanity.com/paper/2409.10704/) (2024)
- [Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification](https://mlsanity.com/paper/2403.00293/) (2024)
- [Robust Channel Learning for Large-Scale Radio Speaker Verification](https://mlsanity.com/paper/2406.10956/) (2024)
- [UniPET-SPK: A Unified Framework for Parameter-Efficient Tuning of Pre-trained Speech Models for Robust Speaker Verification](https://mlsanity.com/paper/2501.16542/) (2025)
- [G-IFT: A Gated Linear Unit adapter with Iterative Fine-Tuning for Low-Resource Children's Speaker Verification](https://mlsanity.com/paper/2508.07836/) (2025)

## Abstract (arXiv)

Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks. However, most fine-tuning approaches update all the parameters of the pre-trained model, which becomes prohibitive as the model size grows and sometimes results in overfitting on small datasets. In this paper, we conduct a comprehensive analysis of applying parameter-efficient transfer learning (PETL) methods to reduce the required learnable parameters for adapting to speaker verification tasks. Specifically, during the fine-tuning process, the pre-trained models are frozen, and only lightweight modules inserted in each Transformer block are trainable (a method known as adapters). Moreover, to boost the performance in a cross-language low-resource scenario, the Transformer model is further tuned on a large intermediate dataset before directly fine-tuning it on a small dataset. With updating fewer than 4% of parameters, (our proposed) PETL-based methods achieve comparable performances with full fine-tuning methods (Vox1-O: 0.55%, Vox1-E: 0.82%, Vox1-H:1.73%).