Understanding the Predictability of Gesture Parameters from Speech and their Perceptual Importance

Gesture behavior is a natural part of human conversation. Much work has focused on removing the need for tedious hand-animation to create embodied conversational agents by designing speech-driven gesture generators. However, these generators often work in a black-box manner, assuming a general relationship between input speech and output motion. As their success remains limited, we investigate in more detail how speech may relate to different aspects of gesture motion. We determine a number of parameters characterizing gesture, such as speed and gesture size, and explore their relationship to the speech signal in a two-fold manner. First, we train multiple recurrent networks to predict the gesture parameters from speech to understand how well gesture attributes can be modeled from speech alone. We find that gesture parameters can be partially predicted from speech, and some parameters, such as path length, being predicted more accurately than others, like velocity. Second, we design a perceptual study to assess the importance of each gesture parameter for producing motion that people perceive as appropriate for the speech. Results show that a degradation in any parameter was viewed negatively, but some changes, such as hand shape, are more impactful than others. A video summarization can be found at https://youtu.be/aw6-_5kmLjY.Comment: To be published in the Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents (IVA 20

Temporally Guided Music-to-Body-Movement Generation

This paper presents a neural network model to generate virtual violinist's 3-D skeleton movements from music audio. Improved from the conventional recurrent neural network models for generating 2-D skeleton data in previous works, the proposed model incorporates an encoder-decoder architecture, as well as the self-attention mechanism to model the complicated dynamics in body movement sequences. To facilitate the optimization of self-attention model, beat tracking is applied to determine effective sizes and boundaries of the training examples. The decoder is accompanied with a refining network and a bowing attack inference mechanism to emphasize the right-hand behavior and bowing attack timing. Both objective and subjective evaluations reveal that the proposed model outperforms the state-of-the-art methods. To the best of our knowledge, this work represents the first attempt to generate 3-D violinists' body movements considering key features in musical body movement

Generating Gestures from Speech

This article describes a first version of a system for translating speech into Spanish Sign Language. The system proposed is made up of 4 modules: speech recognizer, semantic analysis, gesture sequence generation and gesture playing. For the speech recognizer and the semantic analysis, we use modules developed by IBM and the University of Colorado respectively. The gesture sequence generation uses the semantic concepts (obtained in the semantic analysis) associating them to several Spanish Sign Language gestures. This association is carried out based on a number of generating rules. For gesture animation, we have developed an animated character and a strategy for reducing the effort in gesture generation. This strategy consists of making the system generate automatically all agent positions necessary for the gesture animation. In this process, the system uses a few main agent positions (2-3 per second) and some interpolation strategies, both issues previously generated by the service developer. 1