Parameter-Efficient Transfer Learning of Pre-Trained Transformer Models for Speaker Verification Using Adapters

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 over-
fitting 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%).