Clearly 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.
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