  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.
