| Autor: |
Robin Algayres, Tristan Ricoul, Julien Karadayi, Hugo Laurençon, Salah Zaiem, Abdelrahman Mohamed, Benoît Sagot, Emmanuel Dupoux |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Transactions of the Association for Computational Linguistics, Vol 10, Pp 1051-1065 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2307-387X |
| DOI: |
10.1162/tacl_a_00505/113018/DP-Parse-Finding-Word-Boundaries-from-Raw-Speech |
| Popis: |
AbstractFinding word boundaries in continuous speech is challenging as there is little or no equivalent of a ‘space’ delimiter between words. Popular Bayesian non-parametric models for text segmentation (Goldwater et al., 2006, 2009) use a Dirichlet process to jointly segment sentences and build a lexicon of word types. We introduce DP-Parse, which uses similar principles but only relies on an instance lexicon of word tokens, avoiding the clustering errors that arise with a lexicon of word types. On the Zero Resource Speech Benchmark 2017, our model sets a new speech segmentation state-of-the-art in 5 languages. The algorithm monotonically improves with better input representations, achieving yet higher scores when fed with weakly supervised inputs. Despite lacking a type lexicon, DP-Parse can be pipelined to a language model and learn semantic and syntactic representations as assessed by a new spoken word embedding benchmark. 1 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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