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

TL;DR

This study investigates how speech relates to gesture parameters and their perceptual importance, revealing partial predictability of gestures from speech and varying impacts of gesture features on perception.

Contribution

It introduces a detailed analysis of gesture parameters' predictability from speech and their perceptual significance, advancing understanding beyond black-box gesture generators.

Findings

Gesture parameters can be partially predicted from speech.

Some parameters, like path length, are predicted more accurately.

Degradation of any gesture parameter negatively affects perceived appropriateness.

Abstract

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.