GIST-AiTeR Speaker Diarization System for VoxCeleb Speaker Recognition Challenge (VoxSRC) 2023

Autor:	Park, Dongkeon, Kim, Ji Won, Kim, Kang Ryeol, Lee, Do Hyun, Kim, Hong Kook
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	This report describes the submission system by the GIST-AiTeR team for the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23) Track 4. Our submission system focuses on implementing diverse speaker diarization (SD) techniques, including ResNet293 and MFA-Conformer with different combinations of segment and hop length. Then, those models are combined into an ensemble model. The ResNet293 and MFA-Conformer models exhibited the diarization error rates (DERs) of 3.65% and 3.83% on VAL46, respectively. The submitted ensemble model provided a DER of 3.50% on VAL46, and consequently, it achieved a DER of 4.88% on the VoxSRC-23 test set. Comment: VoxSRC 2023 Track4
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.07788 Zobrazit plný text záznamu View this record from Arxiv