Deep Multimodal Semantic Embeddings for Speech and Images

11 Nov 2015 David Harwath James Glass

In this paper, we present a model which takes as input a corpus of images with relevant spoken captions and finds a correspondence between the two modalities. We employ a pair of convolutional neural networks to model visual objects and speech signals at the word level, and tie the networks together with an embedding and alignment model which learns a joint semantic space over both modalities... (read more)

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