Retrieving Target Gestures Toward Speech Driven Animation with Meaningful Behaviors

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.