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Speech2Face: Learning the Face Behind a Voice

CVPR 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Margin-based face recognition losses · 178 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use 6-second rather than 3-second audio at test time (ablation)Speech2Face · AVSpeech test, 5,000-image retrievalR@1 (%): 5.86 -> 8.28 (L2); 6.22 -> 8.34 (L1); 8.54 -> 10.92 (cos)Helped
Use 6-second rather than 3-second audio at test time (ablation)Speech2Face · AVSpeech test, predicted versus true face featuresL1 distance: $9.81 \pm 1.74$ -> $9.42 \pm 1.54$Helped
Train with 6-second rather than 3-second audio (ablation)Speech2Face · AVSpeech training and reconstructiontraining convergence and reconstruction qualityNo clear effect
Add batch normalization (ablation)Speech2Face voice encoder · AVSpeech reconstructionfacial information in reconstructionsHelped
Use feature-space losses instead of only pixel-level image L1 loss (ablation)Speech2Face voice encoder · AVSpeech reconstructionreconstruction qualityHelped
Add losses on the face decoder's first layer and VGG-Face's final layer (ablation)Speech2Face voice encoder · AVSpeech trainingtraining stability and reconstruction qualityHelped
Use Speech2Face audio-to-face retrieval rather than random retrievalSpeech2Face · AVSpeech test, 5,000-image retrievalR@1 (%): 1.00 -> 5.86 (3 sec, L2); 1.00 -> 8.28 (6 sec, L2)Helped

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