Computers cannot fully understand spoken language without access to the
wide range of modalities that accompany speech. This thesis addresses the
particularly expressive modality of hand gesture, and focuses on building
structured statistical models at the intersection of speech, vision, and
meaning.
My approach is distinguished in two key respects. First, gestural patterns
are leveraged to discover parallel structures in the meaning of the
associated speech. This differs from prior work that attempted to interpret
individual gestures directly, an approach that was prone to a lack of
generality across speakers. Second, I present novel, structured statistical
models for multimodal language processing, which enable learning about
gesture in its linguistic context, rather than in the abstract.
These ideas find successful application in a variety of language processing
tasks: resolving ambiguous noun phrases, segmenting speech into topics, and
producing keyframe summaries of spoken language. In all three cases, the
addition of gestural features -- extracted automatically from video --
yields significantly improved performance over a state-of-the-art text-only
alternative. This marks the first demonstration that hand gesture improves
automatic discourse processing.
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