  In conversational speech, the acoustic signal provides cues that help
listeners disambiguate difficult parses. For automatically parsing spoken
utterances, we introduce a model that integrates transcribed text and
acoustic-prosodic features using a convolutional neural network over energy and
pitch trajectories coupled with an attention-based recurrent neural network
that accepts text and prosodic features. We find that different types of
acoustic-prosodic features are individually helpful, and together give
statistically significant improvements in parse and disfluency detection F1
scores over a strong text-only baseline. For this study with known sentence
boundaries, error analyses show that the main benefit of acoustic-prosodic
features is in sentences with disfluencies, attachment decisions are most
improved, and transcription errors obscure gains from prosody.
