Browse State-of-the-Art › Lipreading

Lipreading

36 papers with code · 8 benchmarks · 8 datasets archive 2025-07-28

Computer Vision

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.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

8 leaderboard tables shown for this task, 8 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
LRS2 (25 rows) Auto-AVSR Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels code Syntology ran 0 of 6 samples · 6 unverified Compare
LRS3-TED (23 rows) LP + Conformer Conformers are All You Need for Visual Speech Recognition — — Compare
Lip Reading in the Wild (22 rows) SyncVSR (Word Boundary) SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End... code — Compare
CAS-VSR-W1k (LRW-1000) (9 rows) SyncVSR (Word Boundary) SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End... code — Compare
CMLR (5 rows) CTC/Attention Visual Speech Recognition for Multiple Languages in the Wild code — Compare
GRID corpus (mixed-speech) (5 rows) CTC/Attention Visual Speech Recognition for Multiple Languages in the Wild code — Compare
LRW-1000 (4 rows) 3D Conv + ResNet-18 + MS-TCN Lipreading using Temporal Convolutional Networks code Syntology ran 1 of 2 samples · 1 unverified Compare
CAS-VSR-S101 (1 row) ES³ Base* ES3: Evolving Self-Supervised Learning of Robust Audio-Visual... — — Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

8 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 36 papers with code (103 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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