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_0: Creating believable behaviors for conversational agents (CAs) is a challenging task, given the complex relationship between speech and various nonverbal behaviors. The two main approaches are rule-based systems, which tend to produce behaviors with limited variations compared to natural interactions, and data-driven systems, which tend to ignore the underlying semantic meaning of the message (e.g., gestures without meaning). We envision a hybrid system, acting as the behavior realization layer in rule-based systems, while exploiting the rich variation in natural interactions. Constrained on a given target gesture (e.g., head nod) and speech signal, the system will generate novel realizations learned from the data, capturing the timely relationship between speech and gestures. An important task in this research is identifying multiple examples of the target gestures in the corpus. This paper proposes a data mining framework for detecting gestures of interest in a motion capture database. First, we train One-class support vector machines (SVMs) to detect candidate segments conveying the target gesture. Second, we use dynamic time alignment kernel (DTAK) to compare the similarity between the examples (i.e., target gesture) and the given segments. We evaluate the approach for five prototypical hand and head gestures showing reasonable performance. These retrieved gestures are then used to train a speech-driven framework based on dynamic Bayesian networks (DBNs) to synthesize these target behaviors.
  @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: Sadoughi al @cite_34 proposed a model to generate speech-driven head and eyebrow movements constrained on discourse functions. The preliminary study tested the constrained model on one session of the IEMOCAP corpus, constraining the models on two dialog acts: , and . The subjective evaluation of the models showed that the behaviors from the constrained models are more preferable, natural and appropriate compared to the unconstrained model. In Sadoughi and Busso @cite_0 , we explored the idea of constraining the models using prototypical behaviors such as head nods. The models were trained with gestures directly retrieved from the corpus by providing few examples.