  Recently, fine-tuning large pre-trained Transformer models using downstream
datasets has received a rising interest. Despite their success, it is still
challenging to disentangle the benefits of large-scale datasets and Transformer
structures from the limitations of the pre-training. In this paper, we
introduce a hierarchical training approach, named self-pretraining, in which
Transformer models are pretrained and finetuned on the same dataset. Three
pre-trained models including HuBERT, Conformer and WavLM are evaluated on four
different speaker verification datasets with varying sizes. Our experiments
show that these self-pretrained models achieve competitive performance on
downstream speaker verification tasks with only one-third of the data compared
to Librispeech pretraining, such as VoxCeleb1 and CNCeleb1. Furthermore, when
pre-training only on the VoxCeleb2-dev, the Conformer model outperforms the one
pre-trained on 94k hours of data using the same fine-tuning settings.
