  In this paper, adaptive mechanisms are applied in deep neural network (DNN)
training for x-vector-based text-independent speaker verification. First,
adaptive convolutional neural networks (ACNNs) are employed in frame-level
embedding layers, where the parameters of the convolution filters are adjusted
based on the input features. Compared with conventional CNNs, ACNNs have more
flexibility in capturing speaker information. Moreover, we replace conventional
batch normalization (BN) with adaptive batch normalization (ABN). By
dynamically generating the scaling and shifting parameters in BN, ABN adapts
models to the acoustic variability arising from various factors such as channel
and environmental noises. Finally, we incorporate these two methods to further
improve performance. Experiments are carried out on the speaker in the wild
(SITW) and VOiCES databases. The results demonstrate that the proposed methods
significantly outperform the original x-vector approach.
