  This work explores constituency parsing on automatically recognized
transcripts of conversational speech. The neural parser is based on a sentence
encoder that leverages word vectors contextualized with prosodic features,
jointly learning prosodic feature extraction with parsing. We assess the
utility of the prosody in parsing on imperfect transcripts, i.e. transcripts
with automatic speech recognition (ASR) errors, by applying the parser in an
N-best reranking framework. In experiments on Switchboard, we obtain 13-15% of
the oracle N-best gain relative to parsing the 1-best ASR output, with
insignificant impact on word recognition error rate. Prosody provides a
significant part of the gain, and analyses suggest that it leads to more
grammatical utterances via recovering function words.
