Paper Detail
Se Un Park, Hakjun Kim, Taehoon Roh, Junyoung Park
We present a personalized Korean visual speech recognition (VSR) system and quantify, on the nine-camera OLKAVS corpus, the gap between the population-level benchmark score and an individual user's error. A video-only Conformer initialized from English-trained weights attains 9.95 - 12.19% character error rate (CER) under the corpus protocol against the published 26.64, and 19.00 - 21.52 on unseen wording. Per speaker, CER spans 1.0 to 52.2%, with seen wording lowering CER by 7.0 - 9.0 points and professional delivery and spontaneous speech raising it by 8.5 - 10.5 and 12.7 points. A low-rank adapter with 4.6% of the parameters, trained on 4 to 29 minutes of the user's frontal video, lowers the CER of twelve high-error speakers by 2.13 to 3.58 points, transfers to every camera without loss, and keeps 85% of the full fine-tuning gain at 12% of its cost to other speakers. Cameras above the mouth plane add about six CER points as a constant offset that training on all views keeps small.
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@article{park2026personalized,
title = {Personalized Korean Lipreading as Visual Speech Recognition: Transfer, Census and Adaptation on OLKAVS},
author = {Se Un Park and Hakjun Kim and Taehoon Roh and Junyoung Park},
year = {2026},
abstract = {We present a personalized Korean visual speech recognition (VSR) system and quantify, on the nine-camera OLKAVS corpus, the gap between the population-level benchmark score and an individual user's error. A video-only Conformer initialized from English-trained weights attains 9.95 - 12.19\% character error rate (CER) under the corpus protocol against the published 26.64, and 19.00 - 21.52 on unseen wording. Per speaker, CER spans 1.0 to 52.2\%, with seen wording lowering CER by 7.0 - 9.0 points an},
url = {https://arxiv.org/abs/2609.28988},
keywords = {eess.AS, cs.CL, cs.CV},
eprint = {2609.28988},
archiveprefix = {arXiv},
}
{}