  Automatic gesture generation from speech generally relies on implicit
modelling of the nondeterministic speech-gesture relationship and can result in
averaged motion lacking defined form. Here, we propose a database-driven
approach of selecting gestures based on specific motion characteristics that
have been shown to be associated with the speech audio. We extend previous work
that identified expressive parameters of gesture motion that can both be
predicted from speech and are perceptually important for a good speech-gesture
match, such as gesture velocity and finger extension. A perceptual study was
performed to evaluate the appropriateness of the gestures selected with our
method. We compare our method with two baseline selection methods. The first
respects timing, the desired onset and duration of a gesture, but does not
match gesture form in other ways. The second baseline additionally disregards
the original gesture timing for selecting gestures. The gesture sequences from
our method were rated as a significantly better match to the speech than
gestures selected by either baseline method.
