  In this paper, we propose TitaNet, a novel neural network architecture for
extracting speaker representations. We employ 1D depth-wise separable
convolutions with Squeeze-and-Excitation (SE) layers with global context
followed by channel attention based statistics pooling layer to map
variable-length utterances to a fixed-length embedding (t-vector). TitaNet is a
scalable architecture and achieves state-of-the-art performance on speaker
verification task with an equal error rate (EER) of 0.68% on the VoxCeleb1
trial file and also on speaker diarization tasks with diarization error rate
(DER) of 1.73% on AMI-MixHeadset, 1.99% on AMI-Lapel and 1.11% on CH109.
Furthermore, we investigate various sizes of TitaNet and present a light
TitaNet-S model with only 6M parameters that achieve near state-of-the-art
results in diarization tasks.
