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 1–25 of 103 papers

TitleStatusHype
Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech ProcessingCode3
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster PredictionCode2
SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token SynchronizationCode2
Training Strategies for Improved Lip-readingCode2
Visual Speech Recognition for Multiple Languages in the WildCode2
Auto-AVSR: Audio-Visual Speech Recognition with Automatic LabelsCode2
Robust Self-Supervised Audio-Visual Speech RecognitionCode2
Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual InputsCode1
Towards Practical Lipreading with Distilled and Efficient ModelsCode1
Watch Your Mouth: Silent Speech Recognition with Depth SensingCode1
Discriminative Multi-modality Speech RecognitionCode1
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery DetectionCode1
LipLearner: Customizable Silent Speech Interactions on Mobile DevicesCode1
Mutual Information Maximization for Effective Lip ReadingCode1
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation ModelsCode1
Jointly Learning Visual and Auditory Speech Representations from Raw DataCode1
Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech RecognitionCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
Deformation Flow Based Two-Stream Network for Lip ReadingCode1
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech RecognitionCode1
LipNet: End-to-End Sentence-level LipreadingCode1
Lipreading using Temporal Convolutional NetworksCode1
Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip ReadingCode1
Deep Audio-Visual Speech RecognitionCode1
Learn an Effective Lip Reading Model without PainsCode1
Show:102550
← PrevPage 1 of 5Next →

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