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

Robin Algayres; Tristan Ricoul; Julien Karadayi; Hugo Laurençon; Salah Zaiem; Abdelrahman Mohamed; Benoît Sagot; Emmanuel Dupoux

arXiv · 2206.11332

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

Robin Algayres·Tristan Ricoul·Julien Karadayi·Hugo Laurençon·Salah Zaiem·Abdelrahman Mohamed·Benoît Sagot·Emmanuel Dupoux

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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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Robin Algayres, Tristan Ricoul, Julien Karadayi, Hugo Laurençon, Salah Zaiem, Abdelrahman Mohamed, Benoît Sagot, Emmanuel Dupoux. 2022-06-22. DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon. https://arxiv.org/abs/2206.11332

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