Phonetically-Aware Coupled Network For Short Duration Text-Independent Speaker Verification.

In this paper we propose an end-to-end phonetically-aware coupled network for short duration speaker verification tasks. Phonetic information is shown to be beneficial for identifying short utterances. A coupled network structure is proposed to exploit phonetic information. The coupled convolutional layers allow the network to provide frame-level supervision based on phonetic representations of the corresponding frames. The end-to-end training scheme using triplet loss function provides direct comparison of speech contents between two utterances and hence enabling phonetic-based normalization. Our systems are compared against the current mainstream speaker verification systems on both NIST SRE and VoxCeleb evaluation datasets. Relative reductions of up to 34% in equal error rate are reported.