LOOK, LISTEN, AND DECODE: MULTIMODAL SPEECH RECOGNITION WITH IMAGES

In this paper, we introduce a multimodal speech recognition scenario, in which an image provides contextual information for a spoken caption to be decoded. We investigate a lattice rescoring algorithm that integrates information from the image at two different points: the image is used to augment the language model with the most likely words, and to rescore the top hypotheses using a word-level RNN. This rescoring mechanism decreases the word error rate by 3 absolute percentage points, compared to a baseline speech recognizer operating with only the speech recording.