Large-scale representation learning from visually grounded untranscribed speech

Systems that learn from associating images with their spoken audio captions are an important step towards visually grounded language acquisition. We describe a scalable method of automatically generating diverse audio data from image caption datasets. This supports pre-training deep networks for encoding both audio and images, by training a dual encoder that learns to align latent representations of both modalities. We fine-tune these models on the Flickr8k Audio Captions Corpus and obtain state-of-the-art retrieval results---improving retrieval in the top 10 from 29.6\% to 49.5\%. We additionally obtain human ratings on model outputs to better assess the impact of incidentally matching image-caption pairs that were not associated in the data, and find that strict corpus based evaluation substantially underestimates the quality of the retrieved results.

• Machine perception

• Natural language processing