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Speaker Identification On Voxceleb1

Metriken

Accuracy
Top-1 (%)

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
Accuracy
Top-1 (%)
Paper TitleRepository
SSAST-PATCH64.264.2SSAST: Self-Supervised Audio Spectrogram Transformer-
AutoSpeech (N=8,C=128)87.6687.66AutoSpeech: Neural Architecture Search for Speaker Recognition-
SSAST-FRAME80.880.8SSAST: Self-Supervised Audio Spectrogram Transformer-
ATST Base (ours)94.394.3ATST: Audio Representation Learning with Teacher-Student Transformer-
M2D ratio=0.694.894.8Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input-
M2D/0.696.596.5Masked Modeling Duo: Towards a Universal Audio Pre-training Framework-
AudioMAE (local)94.894.8Masked Autoencoders that Listen-
MSM-MAE96.696.6Masked Modeling Duo: Towards a Universal Audio Pre-training Framework-
AudioMAE (global)94.194.1Masked Autoencoders that Listen-
M2D/0.796.396.3Masked Modeling Duo: Towards a Universal Audio Pre-training Framework-
COLA37.737.7Contrastive Learning of General-Purpose Audio Representations-
SSAMBA70.170.1SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model-
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Speaker Identification On Voxceleb1 | SOTA | HyperAI