Unsupervised learning of spoken language with visual context
Name
6186-unsupervised-learning-of-spoken-language-with-visual-context.pdf
Description
Published version
Size
4.67 MB
Format
Adobe PDF
Checksum (MD5)
321f243d35500f5f144ced75e293c414
Author(s)
Harwath, David
Torralba, Antonio
Glass, James R.
Date Issued
2017
Journal
Advances in Neural Information Processing Systems 29 (NIPS 2016)
Publisher
Neural Information Processing Systems Foundation, Inc.
Citation
Harwath, David et al. "Unsupervised Learning of Spoken Language with Visual Context." Advances in Neural Information Processing Systems 29 (NIPS 2016), December 2016, Barcelona, Spain, NIPS, 2017. © 2016 NIPS Foundation.
Version
Final published version
Abstract
Humans learn to speak before they can read or write, so why can't computers do the same? In this paper, we present a deep neural network model capable of rudimentary spoken language acquisition using untranscribed audio training data, whose only supervision comes in the form of contextually relevant visual images. We describe the collection of our data comprised of over 120,000 spoken audio captions for the Places image dataset and evaluate our model on an image search and annotation task. We also provide some visualizations which suggest that our model is learning to recognize meaningful words within the caption spectrograms.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/6186-unsupervised-learning-of-spoken-language-with-visual-context
