  Recent work on unsupervised speech segmentation has used self-supervised
models with phone and word segmentation modules that are trained jointly. This
paper instead revisits an older approach to word segmentation: bottom-up
phone-like unit discovery is performed first, and symbolic word segmentation is
then performed on top of the discovered units (without influencing the lower
level). To do this, I propose a new unit discovery model, a new symbolic word
segmentation model, and then chain the two models to segment speech. Both
models use dynamic programming to minimize segment costs from a self-supervised
network with an additional duration penalty that encourages longer units.
Concretely, for acoustic unit discovery, duration-penalized dynamic programming
(DPDP) is used with a contrastive predictive coding model as the scoring
network. For word segmentation, DPDP is applied with an autoencoding recurrent
neural as the scoring network. The two models are chained in order to segment
speech. This approach gives comparable word segmentation results to
state-of-the-art joint self-supervised segmentation models on an English
benchmark. On French, Mandarin, German and Wolof data, it outperforms previous
systems on the ZeroSpeech benchmarks. Analysis shows that the chained DPDP
system segments shorter filler words well, but longer words might require some
external top-down signal.
