RAS-E2E: The SincNet end-to-end with RawNet loss for text-independent speaker verification

Abstract

Despite reaching satisfactory verification performance, variousness utterance duration and phonemes and the robustness of the system remain a challenge in speaker ver-ification tasks. To deal with this challenge, we propose RAS-E2E, a novel fully cross-lingual speaker verification system that discovers meaningful information from input raw waveforms of various duration utterances, including short utterance duration, to determine whether an utterance matches the target speaker by merging two powerful paradigms: SincNet and Rawnet training scheme with Bi-RNN. The conducted experiments on Voxceleb, Gowajee and internal call-center datasets demonstrate that RAS-E2E achieves better performance compared to the recent verification systems on waveforms.