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- W4309584294 abstract "In this report, we describe our submission to the Ego4D AudioVisual (AV) Speech Transcription Challenge 2022. Our pipeline is based on AVATAR, a state of the art encoder-decoder model for AV-ASR that performs early fusion of spectrograms and RGB images. We describe the datasets, experimental settings and ablations. Our final method achieves a WER of 68.40 on the challenge test set, outperforming the baseline by 43.7%, and winning the challenge." @default.
- W4309584294 created "2022-11-28" @default.
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- W4309584294 date "2022-11-17" @default.
- W4309584294 modified "2023-09-27" @default.
- W4309584294 title "AVATAR submission to the Ego4D AV Transcription Challenge" @default.
- W4309584294 doi "https://doi.org/10.48550/arxiv.2211.09966" @default.
- W4309584294 hasPublicationYear "2022" @default.
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