# Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech

arXiv: https://arxiv.org/abs/2406.12621 · HTML: https://mlsanity.com/paper/2406.12621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech](https://mlsanity.com/paper/2309.05472/) (2023)
- [Putting Natural in Natural Language Processing](https://mlsanity.com/paper/2305.04572/) (2023)
- [Bracketing Encodings for 2-Planar Dependency Parsing](https://mlsanity.com/paper/2011.00596/) (2020)
- [A Unifying Theory of Transition-based and Sequence Labeling Parsing](https://mlsanity.com/paper/2011.00584/) (2020)
- [Neural Constituency Parsing of Speech Transcripts](https://mlsanity.com/paper/1904.08535/) (2019)

## Abstract (arXiv)

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.