SOTAVerified

Lipreading

Lipreading is a process of extracting speech by watching lip movements of a speaker in the absence of sound. Humans lipread all the time without even noticing. It is a big part in communication albeit not as dominant as audio. It is a very helpful skill to learn especially for those who are hard of hearing.

Deep Lipreading is the process of extracting speech from a video of a silent talking face using deep neural networks. It is also known by few other names: Visual Speech Recognition (VSR), Machine Lipreading, Automatic Lipreading etc.

The primary methodology involves two stages: i) Extracting visual and temporal features from a sequence of image frames from a silent talking video ii) Processing the sequence of features into units of speech e.g. characters, words, phrases etc. We can find several implementations of this methodology either done in two separate stages or trained end-to-end in one go.

Papers

Showing 26–50 of 103 papers

TitleStatusHype
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
Combining Residual Networks with LSTMs for LipreadingCode0
Deep word embeddings for visual speech recognitionCode0
Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal Fusion Strategy for Driver Assistive SystemsCode0
End-to-end Audiovisual Speech RecognitionCode0
SpotFast Networks with Memory Augmented Lateral Transformers for LipreadingCode0
Recurrent Neural Network Transducer for Audio-Visual Speech RecognitionCode0
Evaluation of End-to-End Continuous Spanish Lipreading in Different Data ConditionsCode0
LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the WildCode0
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading—0
Audio-visual Multi-channel Recognition of Overlapped Speech—0
Decoding visemes: improving machine lipreading—0
Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture—0
ASR is all you need: cross-modal distillation for lip reading—0
Accurate and Resource-Efficient Lipreading with Efficientnetv2 and Transformers—0
Decoding visemes: improving machine lipreading—0
Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading—0
Large-vocabulary Audio-visual Speech Recognition in Noisy Environments—0
Large-Scale Visual Speech Recognition—0
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition—0
Learning from Videos with Deep Convolutional LSTM Networks—0
Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading—0
Learning Speaker-Invariant Visual Features for Lipreading—0
Conformers are All You Need for Visual Speech Recognition—0
Is Lip Region-of-Interest Sufficient for Lipreading?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Conv-seq2seqWord Error Rate (WER)60.1—Unverified
2CTC + KDWord Error Rate (WER)59.8—Unverified
3TM-seq2seqWord Error Rate (WER)58.9—Unverified
4EG-seq2seqWord Error Rate (WER)57.8—Unverified
5CTC-V2PWord Error Rate (WER)55.1—Unverified
6Hyb + ConformerWord Error Rate (WER)43.3—Unverified
7VTPWord Error Rate (WER)40.6—Unverified
8ES³ BaseWord Error Rate (WER)40.3—Unverified
9ES³ LargeWord Error Rate (WER)37.1—Unverified
10RNN-TWord Error Rate (WER)33.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LIBSWord Error Rate (WER)65.29—Unverified
2TM-CTC + extLMWord Error Rate (WER)54.7—Unverified
3CTC + KD ASRWord Error Rate (WER)53.2—Unverified
4Conv-seq2seqWord Error Rate (WER)51.7—Unverified
5Hybrid CTC / AttentionWord Error Rate (WER)50—Unverified
6LF-MMI TDNNWord Error Rate (WER)48.86—Unverified
7TM-seq2seq + extLMWord Error Rate (WER)48.3—Unverified
8Multi-head Visual-Audio MemoryWord Error Rate (WER)44.5—Unverified
9MoCo + wav2vec (w/o extLM)Word Error Rate (WER)43.2—Unverified
10CTC/AttentionWord Error Rate (WER)32.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SyncVSR (Word Boundary)Top-1 Accuracy95—Unverified
23D Conv + ResNet-18 + DC-TCN + KD (Ensemble & Word Boundary)Top-1 Accuracy94.1—Unverified
3SyncVSRTop-1 Accuracy93.2—Unverified
4AVCRFormerTop-1 Accuracy89.57—Unverified
53D Conv + EfficientNetV2 + Transformer + TCNTop-1 Accuracy89.52—Unverified
6Vosk + MediaPipe + LS + MixUp + SA + 3DResNet-18 + BiLSTM + Cosine WRTop-1 Accuracy88.7—Unverified
73D Conv + ResNet-18 + MS-TCN + Multi-Head Visual-Audio MemoryTop-1 Accuracy88.5—Unverified
83D Conv + ResNet-18 + MS-TCN + KD (Ensemble)Top-1 Accuracy88.5—Unverified
93D-ResNet + Bi-GRU + MixUp + Label Smoothing + Cosine LR (Word Boundary)Top-1 Accuracy88.4—Unverified
103D-ResNet + Bi-GRU + MixUp + Label Smoothing + Cosine LRTop-1 Accuracy85.5—Unverified
#ModelMetricClaimedVerifiedStatus
1SyncVSR (Word Boundary)Top-1 Accuracy58.2—Unverified
23D-ResNet + Bi-GRU + MixUp + Label Smooth + Cosine LR (Word Boundary)Top-1 Accuracy55.7—Unverified
33D Conv + ResNet-18 + MS-TCN + Multi-Head Visual-Audio MemoryTop-1 Accuracy53.8—Unverified
43D Conv + ResNet-18 + Bi-GRU + Visual-Audio MemoryTop-1 Accuracy50.82—Unverified
53D-ResNet + Bi-GRU + MixUp + Label Smooth + Cosine LRTop-1 Accuracy48.3—Unverified
63D Conv + ResNet-18 + Bi-GRU (Face Cutout)Top-1 Accuracy45.24—Unverified
7DFTNTop-1 Accuracy41.93—Unverified
8GLMIMTop-1 Accuracy38.79—Unverified
9PCPGTop-1 Accuracy38.7—Unverified
#ModelMetricClaimedVerifiedStatus
1WASCER38.93—Unverified
2LipCH-NetCER34.07—Unverified
3CSSMCMCER32.48—Unverified
4LIBSCER31.27—Unverified
5CTC/AttentionCER9.1—Unverified
#ModelMetricClaimedVerifiedStatus
1LipNetWord Error Rate (WER)4.6—Unverified
2WASWord Error Rate (WER)3—Unverified
3LCANetWord Error Rate (WER)2.9—Unverified
4LipNet (with Face Cutout)Word Error Rate (WER)2.9—Unverified
5CTC/AttentionWord Error Rate (WER)1.2—Unverified
#ModelMetricClaimedVerifiedStatus
13D Conv + ResNet-18 + MS-TCNTop-1 Accuracy41.4—Unverified
23D Conv + ResNet-34 + Bi-GRUTop-1 Accuracy38.19—Unverified
3DenseNet3D + Bi-GRUTop-1 Accuracy34.76—Unverified
4Multi-Tower LSTM-5Top-1 Accuracy25.76—Unverified
#ModelMetricClaimedVerifiedStatus
1ES³ Base*Word Error Rate (WER)55.6—Unverified