Sachdeva, ShubamRuan, HaoyaoHamarneh, GhassanBehne, Dawn M.Jongman, AllardSereno, Joan A.Wang, Yue2023-04-102023-04-102023-01-28Sachdeva, S., Ruan, H., Hamarneh, G. et al. Plain-to-clear speech video conversion for enhanced intelligibility. Int J Speech Technol 26, 163–184 (2023). https://doi.org/10.1007/s10772-023-10018-zhttps://hdl.handle.net/1808/34078Clearly articulated speech, relative to plain-style speech, has been shown to improve intelligibility. We examine if visible speech cues in video only can be systematically modified to enhance clear-speech visual features and improve intelligibility. We extract clear-speech visual features of English words varying in vowels produced by multiple male and female talkers. Via a frame-by-frame image-warping based video generation method with a controllable parameter (displacement factor), we apply the extracted clear-speech visual features to videos of plain speech to synthesize clear speech videos. We evaluate the generated videos using a robust, state of the art AI Lip Reader as well as human intelligibility testing. The contributions of this study are: (1) we successfully extract relevant visual cues for video modifications across speech styles, and have achieved enhanced intelligibility for AI; (2) this work suggests that universal talker-independent clear-speech features may be utilized to modify any talker’s visual speech style; (3) we introduce “displacement factor” as a way of systematically scaling the magnitude of displacement modifications between speech styles; and (4) the high definition generated videos make them ideal candidates for human-centric intelligibility and perceptual training studies.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License.http://creativecommons.org/licenses/by/4.0/Video speech synthesisSpeech styleIntelligibilityAI lip readingSpeech enhancementArticle10.1007/s10772-023-10018-zhttps://orcid.org/0000-0003-3862-3767PMC10042924openAccess