Time-Domain Speaker Verification Using Temporal Convolutional Networks

Recently, speaker verification systems using deep neural networks have been widely studied. Many of them utilize hand-crafted features such as mel-filterbank energies, mel-frequency cepstral coefficients, and magnitude spectrograms, which are not designed specifically for the speaker verification task and may not be optimal. Recent releases of the large datasets such as VoxCeleb enable us to extract the task-specific features in a data-driven way. In this paper, we propose a speaker verification system that takes the time-domain raw waveforms as inputs, which adopts a learnable encoder and temporal convolutional networks (TCNs) that have shown impressive performance in speech separation. Moreover, we have applied the squeeze and excitation networks after each TCN block to apply channel-wise attention. Our experiments on the VoxCeleb1 dataset demonstrate that the speaker verification system utilizing the proposed feature extraction model outperforms previously proposed time-domain speaker verification systems.