Gist-aiter Speaker Diarization System For Voxceleb Speaker Recognition Challenge (voxsrc) 2023

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keypark2023gist

Related papers

• Microsoft Speaker Diarization System For The Voxceleb Speaker Recognition Challenge 2020 (2020)11.93
• The DKU-MSXF Diarization System For The Voxceleb Speaker Recognition Challenge 2023 (2023)5.24
• The Kriston AI System For The Voxceleb Speaker Recognition Challenge 2022 (2022)0.00
• The HUAWEI Speaker Diarisation System For The Voxceleb Speaker Diarisation Challenge (2020)0.00
• The BUCEA Speaker Diarization System For The Voxceleb Speaker Recognition Challenge 2022 (2022)0.00
• The Xx205 System For The Voxceleb Speaker Recognition Challenge 2020 (2020)0.00
• The DKU-MSXF Speaker Verification System For The Voxceleb Speaker Recognition Challenge 2023 (2023)0.00
• The Newsbridge -telecom Sudparis Voxceleb Speaker Recognition Challenge 2022 System Description (2023)0.00