  Direct dependency parsing of the speech signal -- as opposed to parsing
speech transcriptions -- has recently been proposed as a task (Pupier et al.
2022), as a way of incorporating prosodic information in the parsing system and
bypassing the limitations of a pipeline approach that would consist of using
first an Automatic Speech Recognition (ASR) system and then a syntactic parser.
In this article, we report on a set of experiments aiming at assessing the
performance of two parsing paradigms (graph-based parsing and sequence labeling
based parsing) on speech parsing. We perform this evaluation on a large
treebank of spoken French, featuring realistic spontaneous conversations. Our
findings show that (i) the graph based approach obtain better results across
the board (ii) parsing directly from speech outperforms a pipeline approach,
despite having 30% fewer parameters.
