  We propose SpeakerNet - a new neural architecture for speaker recognition and
speaker verification tasks. It is composed of residual blocks with 1D
depth-wise separable convolutions, batch-normalization, and ReLU layers. This
architecture uses x-vector based statistics pooling layer to map
variable-length utterances to a fixed-length embedding (q-vector). SpeakerNet-M
is a simple lightweight model with just 5M parameters. It doesn't use voice
activity detection (VAD) and achieves close to state-of-the-art performance
scoring an Equal Error Rate (EER) of 2.10% on the VoxCeleb1 cleaned and 2.29%
on the VoxCeleb1 trial files.
