Deep Multimodal Semantic Embeddings for Speech and Images

David Harwath; James Glass

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arXiv · 1511.03690

Deep Multimodal Semantic Embeddings for Speech and Images

David Harwath·James Glass

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Abstract

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. We evaluate our model using image search and annotation tasks on the Flickr8k dataset, which we augmented by collecting a corpus of 40,000 spoken captions using Amazon Mechanical Turk.

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BibTeX RIS

David Harwath, James Glass. 2015-11-11. Deep Multimodal Semantic Embeddings for Speech and Images. https://arxiv.org/abs/1511.03690

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