Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TitaNet: Neural Model for speaker representation with 1D Depth-wise
  separable convolutions and global context   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.

9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg