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In this paper, we propose TitaNet, a novel neural network architecture forextracting speaker representations. We employ 1D depth-wise separableconvolutions with Squeeze-and-Excitation (SE) layers with global contextfollowed by channel attention based statistics pooling layer to mapvariable-length utterances to a fixed-length embedding (t-vector). TitaNet is ascalable architecture and achieves state-of-the-art performance on speakerverification task with an equal error rate (EER) of 0.68% on the VoxCeleb1trial 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 lightTitaNet-S model with only 6M parameters that achieve near state-of-the-artresults in diarization tasks.

TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context