LRS-3
Emerging31papers using it
2022first seen
LRS-3 is a dataset used to evaluate audio-visual speech recognition (AVSR) systems, containing diverse spoken conversations with human-annotated transcriptions.
Papers using LRS-3 (31)
- DiVISe: Direct Visual-Input Speech Synthesis Preserving Speaker
Characteristics And IntelligibilityMMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech TokensLarge Language Models are Strong Audio-Visual Speech Recognition
LearnersVisG AV-HuBERT: Viseme-Guided AV-HuBERTLRS-VoxMM: A benchmark for in-the-wild audio-visual speech recognitionPay Attention to CTC: Fast and Robust Pseudo-Labelling for Unified Speech RecognitionNoise-Robust AV-ASR Using Visual Features Both in the Whisper Encoder and DecoderMoME: Mixture of Matryoshka Experts for Audio-Visual Speech RecognitionTraining Strategies for Modality Dropout Resilient Multi-Modal Target Speaker ExtractionOnline Audio-Visual Autoregressive Speaker ExtractionLipDiffuser: Lip-to-Speech Generation with Conditional Diffusion ModelsReading to Listen at the Cocktail Party: Multi-Modal Speech SeparationLearning Audio-Visual Speech Representation by Masked Multimodal Cluster
PredictionLip-to-Speech Synthesis in the Wild with Multi-task Learningu-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer
to Unlabeled ModalityJointly Learning Visual and Auditory Speech Representations from Raw
DataReVISE: Self-Supervised Speech Resynthesis with Visual Input for
Universal and Generalized Speech EnhancementRobust Self-Supervised Audio-Visual Speech RecognitionImaginary Voice: Face-styled Diffusion Model for Text-to-SpeechCross-Modal Global Interaction and Local Alignment for Audio-Visual
Speech RecognitionSVTS: Scalable Video-to-Speech SynthesisAV-data2vec: Self-supervised Learning of Audio-Visual Speech
Representations with Contextualized Target RepresentationsLipVoicer: Generating Speech from Silent Videos Guided by Lip ReadingSeeing Through the Conversation: Audio-Visual Speech Separation based on
Diffusion ModelVisual Context-driven Audio Feature Enhancement for Robust End-to-End
Audio-Visual Speech RecognitionAuto-AVSR: Audio-Visual Speech Recognition with Automatic LabelsAudio-Visual Speech Separation in Noisy Environments with a Lightweight
Iterative ModelAV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech RecognitionLiteVSR: Efficient Visual Speech Recognition by Learning from Speech
Representations of Unlabeled DataMultilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast
ConformerWhisper-Flamingo: Integrating Visual Features into Whisper for
Audio-Visual Speech Recognition and Translation