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During language acquisition, infants have the benefit of visual cues toground spoken language. Robots similarly have access to audio and visualsensors. Recent work has shown that images and spoken captions can be mappedinto a meaningful common space, allowing images to be retrieved using speechand vice versa. In this setting of images paired with untranscribed spokencaptions, we consider whether computer vision systems can be used to obtaintextual labels for the speech. Concretely, we use an image-to-words multi-labelvisual classifier to tag images with soft textual labels, and then train aneural network to map from the speech to these soft targets. We show that theresulting speech system is able to predict which words occur in anutterance---acting as a spoken bag-of-words classifier---without seeing anyparallel speech and text. We find that the model often confuses semanticallyrelated words, e.g. "man" and "person", making it even more effective as asemantic keyword spotter.

Visually grounded learning of keyword prediction from untranscribed speech