Semantic keyword spotting by learning from images and speech

We consider the problem of representing semantic concepts in speech by learning from untranscribed speech paired with images of scenes. This setting is relevant in low-resource speech processing, robotics, and human language acquisition research. We use an external image tagger to generate soft labels, which serve as targets for training a neural model that maps speech to keyword labels. We introduce a newly collected data set of human semantic relevance judgements and an associated task, semantic keyword spotting, where the goal is to search for spoken utterances that are semantically relevant to a given text query. Without seeing any text, the model trained on parallel speech and images achieves a precision of almost 60% on its top ten semantic retrievals. Compared to a model trained on transcriptions, our model matches human judgements better by some measures, especially in retrieving non-verbatim semantic matches.1