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Finding word boundaries in continuous speech is challenging as there islittle or no equivalent of a 'space' delimiter between words. Popular Bayesiannon-parametric models for text segmentation use a Dirichlet process to jointlysegment sentences and build a lexicon of word types. We introduce DP-Parse,which uses similar principles but only relies on an instance lexicon of wordtokens, avoiding the clustering errors that arise with a lexicon of word types.On the Zero Resource Speech Benchmark 2017, our model sets a new speechsegmentation state-of-the-art in 5 languages. The algorithm monotonicallyimproves with better input representations, achieving yet higher scores whenfed with weakly supervised inputs. Despite lacking a type lexicon, DP-Parse canbe pipelined to a language model and learn semantic and syntacticrepresentations as assessed by a new spoken word embedding benchmark.

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