  In this paper, we present a method for learning discrete linguistic units by
incorporating vector quantization layers into neural models of visually
grounded speech. We show that our method is capable of capturing both
word-level and sub-word units, depending on how it is configured. What
differentiates this paper from prior work on speech unit learning is the choice
of training objective. Rather than using a reconstruction-based loss, we use a
discriminative, multimodal grounding objective which forces the learned units
to be useful for semantic image retrieval. We evaluate the sub-word units on
the ZeroSpeech 2019 challenge, achieving a 27.3\% reduction in ABX error rate
over the top-performing submission, while keeping the bitrate approximately the
same. We also present experiments demonstrating the noise robustness of these
units. Finally, we show that a model with multiple quantizers can
simultaneously learn phone-like detectors at a lower layer and word-like
detectors at a higher layer. We show that these detectors are highly accurate,
discovering 279 words with an F1 score of greater than 0.5.
