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

SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification