  In this paper, we show that representations capturing syllabic units emerge
when training a self-supervised speech model with a visually-grounded training
objective. We demonstrate that a nearly identical model architecture (HuBERT)
trained with a masked language modeling loss does not exhibit this same
ability, suggesting that the visual grounding objective is responsible for the
emergence of this phenomenon. We propose the use of a minimum cut algorithm to
automatically predict syllable boundaries in speech, followed by a 2-stage
clustering method to group identical syllables together. We show that our model
not only outperforms a state-of-the-art syllabic segmentation method on the
language it was trained on (English), but also generalizes in a zero-shot
fashion to Estonian. Finally, we show that the same model is capable of
zero-shot generalization for a word segmentation task on 4 other languages from
the Zerospeech Challenge, in some cases beating the previous state-of-the-art.
