SOTAVerified

Audio-Visual Speech Recognition

Audio-visual speech recognition is the task of transcribing a paired audio and visual stream into text.

Papers

Showing 7180 of 100 papers

TitleStatusHype
Audio Visual Speech Recognition using Deep Recurrent Neural Networks0
Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture0
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading0
AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition0
AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations0
Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs0
Building a synchronous corpus of acoustic and 3D facial marker data for adaptive audio-visual speech synthesis0
Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides0
SynesLM: A Unified Approach for Audio-visual Speech Recognition and Translation via Language Model and Synthetic Data0
RUSAVIC Corpus: Russian Audio-Visual Speech in Cars0
Show:102550
← PrevPage 8 of 10Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Hybrid CTC / AttentionWord Error Rate (WER)39.1Unverified
2TM-Seq2seqTest WER8.5Unverified
3TM-CTCTest WER8.2Unverified
4CTC/AttentionTest WER7Unverified
5CTC/AttentionTest WER1.5Unverified
6Whisper-FlamingoTest WER1.4Unverified
#ModelMetricClaimedVerifiedStatus
1Hyb-ConformerWord Error Rate (WER)2.3Unverified
2Zero-AVSRWord Error Rate (WER)1.5Unverified
3AV-HuBERT LargeWord Error Rate (WER)1.4Unverified
4Whisper-FlamingoWord Error Rate (WER)0.76Unverified
5MMS-LLaMAWord Error Rate (WER)0.74Unverified
#ModelMetricClaimedVerifiedStatus
1AVCRFormerTop-1 Accuracy98.81Unverified
22DCNN + BiLSTM + ResNet + MLFTop-1 Accuracy98.76Unverified
3PBLTop-1 Accuracy98.3Unverified
#ModelMetricClaimedVerifiedStatus
1ES³ Base*Word Error Rate (WER)11Unverified