Abstract / truncated to 115 words (read the full abstract)

In this dissertation, new methods for audiovisual speech synthesis using Hidden Markov Models (HMMs) are presented and their properties are investigated. The problem of audiovisual speech synthesis is to computationally generate both audible speech as well as a matching facial animation or video (a “visual speech signal”) for any given input text. This results in “talking heads” that can read any text to a user, with applications ranging from virtual agents in human-computer interaction to characters in animated films and computer games. For recording and playback of facial motion, an optical marker-based facial motion capturing hardware system and 3D animation software are employed, which represent the state of the art in the animation industry. For ... toggle 3 keywords

speech synthesis – audiovisual speech synthesis – hidden markov models

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