What dataset was used in this challenge?
SRE04/05/06/08/10/MIXER6
LDC98S75/LDC99S79/LDC2002S06/LDC2001S13/LDC2004S07
Voxceleb 1/2
Fisher + Switchboard I
Callhome+Callfriend
This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge. Six subsystems, including etdnn/ams, ftdnn/as, eftdnn/ams, resnet, multitask and c-vector are developed in this evaluation.
This paper describes the systems developed by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) for the NIST 2019 speaker recognition evaluation (SRE) CTS challenge BIBREF0. Six subsystems, including etdnn/ams, ftdnn/as, eftdnn/ams, resnet, multitask and c-vector are developed in this evaluation. All the subsystems consists of a deep neural network followed by dimension deduction, score normalization and calibration. For each system, we begin with a summary of the data usage, followed by a description of the system setup along with their hyperparameters. Finally, we report experimental results obtained by each subsystem and fusion system on the SRE18 development and SRE18 evaluation datasets.