  The speech representations learned from large-scale unlabeled data have shown
better generalizability than those from supervised learning and thus attract a
lot of interest to be applied for various downstream tasks. In this paper, we
explore the limits of speech representations learned by different
self-supervised objectives and datasets for automatic speaker verification
(ASV), especially with a well-recognized SOTA ASV model, ECAPA-TDNN [1], as a
downstream model. The representations from all hidden layers of the pre-trained
model are firstly averaged with learnable weights and then fed into the
ECAPA-TDNN as input features. The experimental results on Voxceleb dataset show
that the weighted average representation is significantly superior to FBank, a
conventional handcrafted feature for ASV. Our best single system achieves
0.537%, 0.569%, and 1.180% equal error rate (EER) on the three official trials
of VoxCeleb1, separately. Accordingly, the ensemble system with three
pre-trained models can further improve the EER to 0.479%, 0.536% and 1.023%.
Among the three evaluation trials, our best system outperforms the winner
system [2] of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC2021) on
the VoxCeleb1-E trial.
