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AVATAR submission to the Ego4D AV Transcription Challenge

arXiv 2211.09966 ↗First posted 18 Nov 2022cs.CV cs.MM cs.SDcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 18 Nov 2022.

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10 of 16 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,556
  3. 2019HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clipscited by 950
  4. 2021Attention Bottlenecks for Multimodal Fusioncited by 361
  5. 2018Deep Audio-Visual Speech Recognitioncited by 322
  6. 2016Lip Reading Sentences in the Wildcited by 228
  7. 2021End-to-end Audio-visual Speech Recognition with Conformerscited by 151
  8. 2019Recurrent Neural Network Transducer for Audio-Visual Speech Recognitioncited by 74
  9. 2018End-to-end Audiovisual Speech Recognitioncited by 73
  10. 2022AVATAR: Unconstrained Audiovisual Speech Recognitioncited by 13

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