  Speaker recognition systems based on Convolutional Neural Networks (CNNs) are
often built with off-the-shelf backbones such as VGG-Net or ResNet. However,
these backbones were originally proposed for image classification, and
therefore may not be naturally fit for speaker recognition. Due to the
prohibitive complexity of manually exploring the design space, we propose the
first neural architecture search approach approach for the speaker recognition
tasks, named as AutoSpeech. Our algorithm first identifies the optimal
operation combination in a neural cell and then derives a CNN model by stacking
the neural cell for multiple times. The final speaker recognition model can be
obtained by training the derived CNN model through the standard scheme. To
evaluate the proposed approach, we conduct experiments on both speaker
identification and speaker verification tasks using the VoxCeleb1 dataset.
Results demonstrate that the derived CNN architectures from the proposed
approach significantly outperform current speaker recognition systems based on
VGG-M, ResNet-18, and ResNet-34 back-bones, while enjoying lower model
complexity.
