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Models/VQ-MAE-S-12 (Frame) + Query2Emo

VQ-MAE-S-12 (Frame) + Query2Emo

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Audio4 results

Emotion RecognitiononEmoDB Dataset
Accuracy· 2023-04-21
90.2
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117
Emotion RecognitiononEmoDB Dataset
F1· 2023-04-21
0.891
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117
Emotion RecognitiononRAVDESS
Accuracy· 2023-04-21
84.1
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117
Emotion RecognitiononRAVDESS
F1· 2023-04-21
0.844
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117

Speech4 results

Speech Emotion RecognitiononEmoDB Dataset
Accuracy· 2023-04-21
90.2
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117
Speech Emotion RecognitiononEmoDB Dataset
F1· 2023-04-21
0.891
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117
Speech Emotion RecognitiononRAVDESS
Accuracy· 2023-04-21
84.1
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117
Speech Emotion RecognitiononRAVDESS
F1· 2023-04-21
0.844
SOTA
A vector quantized masked autoencoder for speech emotion recognition arXiv:2304.11117