  A back-end model is a key element of modern speaker verification systems.
Probabilistic linear discriminant analysis (PLDA) has been widely used as a
back-end model in speaker verification. However, it cannot fully make use of
multiple utterances from enrollment speakers. In this paper, we propose a novel
attention-based back-end model, which can be used for both text-independent
(TI) and text-dependent (TD) speaker verification with multiple enrollment
utterances, and employ scaled-dot self-attention and feed-forward
self-attention networks as architectures that learn the intra-relationships of
the enrollment utterances. In order to verify the proposed attention back-end,
we combine it with two completely different but dominant speaker encoders,
which are time delay neural network (TDNN) and ResNet trained using the
additive-margin-based softmax loss and the uniform loss, and compare them with
the conventional PLDA or cosine scoring approaches. Experimental results on a
multi-genre dataset called CN-Celeb show that the performance of our proposed
approach outperforms PLDA scoring with TDNN and cosine scoring with ResNet by
around 14.1% and 7.8% in relative EER, respectively. Additionally, an ablation
experiment is also reported in this paper for examining the impact of some
significant hyper-parameters for the proposed back-end model.
