  Most of the recent state-of-the-art results for speaker verification are
achieved by X-vector and its subsequent variants. In this paper, we propose a
new network architecture which aggregates the channel and context
interdependence features from multi aspect based on Time Delay Neural Network
(TDNN). Firstly, we use the SE-Res2Blocks as in ECAPA-TDNN to explicitly model
the channel interdependence to realize adaptive calibration of channel
features, and process local context features in a multi-scale way at a more
granular level compared with conventional TDNN-based methods. Secondly, we
explore to use the encoder structure of Transformer to model the global context
interdependence features at an utterance level which can capture better long
term temporal characteristics. Before the pooling layer, we aggregate the
outputs of SE-Res2Blocks and Transformer encoder to leverage the complementary
channel and context interdependence features learned by themself respectively.
Finally, instead of performing a single attentive statistics pooling, we also
find it beneficial to extend the pooling method in a multi-head way which can
discriminate features from multiple aspect. The proposed MACCIF-TDNN
architecture can outperform most of the state-of-the-art TDNN-based systems on
VoxCeleb1 test sets.
