Minimized Database of Unit Selection in Visual Speech Synthesis without Loss of Naturalness

摘要

Image-based modeling is very successful in the creation of realistic facial animations. Applications with dialog systems, such as e-Learning and customer information service, can integrate facial animations with synthesized speech in websites to improve human-machine communication. However, downloading a database with 11,594 mouth images (about 120MB in JPEG format) used by talking head needs about 15 minutes at 150 kBps. This paper presents a prototype framework of two-step database minimization. First, the key mouth images are identified by clustering algorithms and similar mouth images are discarded. Second, the clustered key mouth images are further compressed by JPEG. MST (Minimum Spanning Tree), RSST (Recursive Shortest Spanning Tree) and LBG-based clustering algorithms are developed and evaluated. Our experiments demonstrate that the number of mouth images is lowered by the LBG-based clustering algorithm and further compressed to 8MB by JPEG, which generates facial animations in GIF format without loss of naturalness and fulfill the need of talking head for Internet applications.