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Current speaker verification techniques rely on a neural network to extractspeaker representations. The successful x-vector architecture is a Time DelayNeural Network (TDNN) that applies statistics pooling to projectvariable-length utterances into fixed-length speaker characterizing embeddings.In this paper, we propose multiple enhancements to this architecture based onrecent trends in the related fields of face verification and computer vision.Firstly, the initial frame layers can be restructured into 1-dimensionalRes2Net modules with impactful skip connections. Similarly to SE-ResNet, weintroduce Squeeze-and-Excitation blocks in these modules to explicitly modelchannel interdependencies. The SE block expands the temporal context of theframe layer by rescaling the channels according to global properties of therecording. Secondly, neural networks are known to learn hierarchical features,with each layer operating on a different level of complexity. To leverage thiscomplementary information, we aggregate and propagate features of differenthierarchical levels. Finally, we improve the statistics pooling module withchannel-dependent frame attention. This enables the network to focus ondifferent subsets of frames during each of the channel's statistics estimation.The proposed ECAPA-TDNN architecture significantly outperforms state-of-the-artTDNN based systems on the VoxCeleb test sets and the 2019 VoxCeleb SpeakerRecognition Challenge.

ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification