text: Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.
  @cite_47: We describe a method for using a database of recorded speech and captured motion to create an animated conversational character. People's utterances are composed of short, clearly-delimited phrases; in each phrase, gesture and speech go together meaningfully and synchronize at a common point of maximum emphasis. We develop tools for collecting and managing performance data that exploit this structure. The tools help create scripts for performers, help annotate and segment performance data, and structure specific messages for characters to use within application contexts. Our animations then reproduce this structure. They recombine motion samples with new speech samples to recreate coherent phrases, and blend segments of speech and motion together phrase-by-phrase into extended utterances. By framing problems for utterance generation and synthesis so that they can draw closely on a talented performance, our techniques support the rapid construction of animated characters with rich and appropriate expression.
  @cite_34: Conversational agents provide powerful opportunities to interact and engage with the users. The challenge is how to create naturalistic behaviors that replicate the complex gestures observed during human interactions. Previous studies have used rule-based frameworks or data-driven models to generate appropriate gestures, which are properly synchronized with the underlying discourse functions. Among these methods, speech-driven approaches are especially appealing given the rich information conveyed on speech. It captures emotional cues and prosodic patterns that are important to synthesize behaviors (i.e., modeling the variability and complexity of the timings of the behaviors). The main limitation of these models is that they fail to capture the underlying semantic and discourse functions of the message (e.g., nodding). This study proposes a speech-driven framework that explicitly model discourse functions, bridging the gap between speech-driven and rule-based models. The approach is based on dynamic Bayesian Network (DBN), where an additional node is introduced to constrain the models by specific discourse functions. We implement the approach by synthesizing head and eyebrow motion. We conduct perceptual evaluations to compare the animations generated using the constrained and unconstrained models.
summary: Studies have attempted to combine both approaches creating hybrid frameworks. Stone al @cite_47 designed a hybrid system to generate meaningful behaviors given the text. They jointly segment audio and motion capture recordings into units expressing pre-defined communicative intents. Given an input text, they parse the input into their predefined categories, using dynamic programming to find speech and motion capture segments that have the same communicative goal. The generation with this framework is limited to the stored speech segments. Sadoughi al @cite_34 proposed to constrain a speech-driven model based on the discourse functions of the sentence to generate more meaningful head and eyebrow motion. The study was limited to only two discourse functions: and , where the subjective evaluation of the result showed improvements for the constrained model versus the unconstrained model when the constraint was .