DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon

• URL: http://arxiv.org/abs/2206.11332v1
• Date: Wed, 22 Jun 2022 19:15:57 GMT
• Title: DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon
• Authors: Robin Algayres, Tristan Ricoul, Julien Karadayi, Hugo Lauren\c{c}on, Salah Zaiem, Abdelrahman Mohamed, Beno\^it Sagot, Emmanuel Dupoux
• Abstract summary: We introduce DP-Parse, which uses similar principles but only relies on an instance lexicon of word tokens. On the Zero Resource Speech Benchmark 2017, our model sets a new speech segmentation state-of-the-art in 5 languages. Despite lacking a type lexicon, DP-Parse can be pipelined to a language model and learn and semantic representations as assessed by a new spoken word embedding benchmark.
• Score: 18.05179713472479
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding 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 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.

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