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플랫폼
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스피커 인증
Speaker Verification On Voxceleb
Speaker Verification On Voxceleb
평가 지표
EER
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
EER
Paper Title
Multi Task SSL
1.98
Multi-task Voice Activated Framework using Self-supervised Learning
ReDimNet-B0-LM (1.0M)
1.16
Reshape Dimensions Network for Speaker Recognition
TitanNet -S
1.15
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
ReDimNet-B0-LM-ASNorm (1.0M)
1.07
Reshape Dimensions Network for Speaker Recognition
SpeechNAS
1.02
SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification
ReDimNet-B1-LM (2.2M)
0.85
Reshape Dimensions Network for Speaker Recognition
TitanNet -M
0.81
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
ReDimNet-B1-LM-ASNorm (2.2M)
0.73
Reshape Dimensions Network for Speaker Recognition
TitanNet -L
0.68
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
ReDimNet-B2-SF2-LM (4.7M)
0.57
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B2-SF2-LM-ASNorm (4.7M)
0.52
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B4-LM (6.3M)
0.51
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B3-LM (3.0M)
0.5
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B3-LM-ASNorm (3.0M)
0.47
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B4-LM-ASNorm (6.3M)
0.44
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B5-SF2-LM (9.2M)
0.43
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B6-SF2-LM (15.0M)
0.4
Reshape Dimensions Network for Speaker Recognition
WavLM+ECAPA-TDNN
0.39
ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models
ReDimNet-B5-SF2-LM-ASNorm (9.2M)
0.39
Reshape Dimensions Network for Speaker Recognition
ReDimNet-B6-SF2-LM-ASNorm (15.0M)
0.37
Reshape Dimensions Network for Speaker Recognition
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