Browse State-of-the-Art › Speaker-Specific Lip to Speech Synthesis

Speaker-Specific Lip to Speech Synthesis

3 papers with code · 7 benchmarks · 2 datasets archive 2025-07-28

Computer Vision

How accurately can we infer an individual’s speech style and content from his/her lip movements? [1]

In this task, the model is trained on a specific speaker, or a very limited set of speakers.

[1] Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis, CVPR 2020.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

7 leaderboard tables shown for this task, 7 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
GRID corpus (mixed-speech) (2 rows) Visual Voice Memory Speech Reconstruction with Reminiscent Sound via Visual Voice Memory code — Compare
Lip2Wav (EH) (2 rows) Visual Voice Memory Speech Reconstruction with Reminiscent Sound via Visual Voice Memory code — Compare
Lip2Wav (HS) (2 rows) Visual Voice Memory Speech Reconstruction with Reminiscent Sound via Visual Voice Memory code — Compare
Lip2Wav (DL) (2 rows) Visual Voice Memory Speech Reconstruction with Reminiscent Sound via Visual Voice Memory code — Compare
Lip2Wav (Chess) (2 rows) Visual Voice Memory Speech Reconstruction with Reminiscent Sound via Visual Voice Memory code — Compare
Lip2Wav (Chem) (2 rows) Visual Voice Memory Speech Reconstruction with Reminiscent Sound via Visual Voice Memory code — Compare
TCD-TIMIT corpus (mixed-speech) (1 row) Lip2Wav Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis code Syntology ran 1 of 9 samples · 8 unverified 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

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

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

3 shown of 3 papers with code (4 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.

  • 25 Aug 2016 146 repositories listed Syntology ran 18 of 71 samples · 53 unverified · 7 pointer-only (licence)
    Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
  • 17 Nov 2021 1 repository listed
    Our key contributions are: (1) proposing the Visual Voice memory that brings rich information of audio that complements the visual features, thus producing high-quality speech from silent video, and (2) enabling…
  • 17 May 2020 1 repository listed Syntology ran 1 of 9 samples · 8 unverified
    In this work, we explore the task of lip to speech synthesis, i.

Syntology lines on 2 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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