Expressive Conversational-Behavior Generation Models for Advanced Inte

The aim of the book is to represent a flexible and efficient algorithm and a novel system used for the planning, generation, and realization of conversational behavior (co-verbal behavior). Such behavior is best described as a set of moving body parts, which are meaningful. In terms of prosody, it is synchronized with the accompanying speech. The movement and shapes generated as a co-verbal behavior represent a contextual link between a repertoire of independent motor skills (shapes, movements, and poses that conversational agent can reproduce and execute), and the intent/meaning of spoken sequences (context). The actual intent/meaning of spoken content is identified through language-dependent linguistic markers and prosody. The knowledge databases used to determine the intent/meaning of text are based on the linguistic analysis and classification of the text into semiotic classes and subclasses achieved through annotation of multimodal corpora based on the proposed EVA annotation scheme. The scheme allows for capturing features at a functional (context-dependent), as well as at a descriptive (context-independent) level. The functional level captures high-level features that describe the correlation between speech and co-verbal behavior, whereas the descriptive level allows us to capture and define body-poses and shapes independently of verbal content and in high-resolution. The annotation scheme, therefore, not only interlinks speech and gesture at a semiotic level, but also serves as a basis for the creation of a context independent repertoire of movement and shapes. The process of generating the co-verbal behavior is, in this book, divided into two phases. The first phase deals wi