SOTAVerified

Audio-Visual Speech Recognition

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

Papers

Showing 31–40 of 100 papers

TitleStatusHype
MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth InformationCode1
Should we hard-code the recurrence concept or learn it instead ? Exploring the Transformer architecture for Audio-Visual Speech RecognitionCode1
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech RecognitionCode1
How to Teach DNNs to Pay Attention to the Visual Modality in Speech RecognitionCode1
Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion EncoderCode1
AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition—0
Detecting Adversarial Attacks On Audiovisual Speech Recognition—0
Deep Multimodal Representation Learning from Temporal Data—0
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading—0
Deep Multimodal Learning for Audio-Visual Speech Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Hybrid CTC / AttentionWord Error Rate (WER)39.1—Unverified
2TM-Seq2seqTest WER8.5—Unverified
3TM-CTCTest WER8.2—Unverified
4CTC/AttentionTest WER7—Unverified
5CTC/AttentionTest WER1.5—Unverified
6Whisper-FlamingoTest WER1.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Hyb-ConformerWord Error Rate (WER)2.3—Unverified
2Zero-AVSRWord Error Rate (WER)1.5—Unverified
3AV-HuBERT LargeWord Error Rate (WER)1.4—Unverified
4Whisper-FlamingoWord Error Rate (WER)0.76—Unverified
5MMS-LLaMAWord Error Rate (WER)0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1AVCRFormerTop-1 Accuracy98.81—Unverified
22DCNN + BiLSTM + ResNet + MLFTop-1 Accuracy98.76—Unverified
3PBLTop-1 Accuracy98.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ES³ Base*Word Error Rate (WER)11—Unverified