Learning Word-Like Units from Joint Audio-Visual Analysis

David Harwath, James Glass


Abstract
Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word ‘lighthouse’ within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.
Anthology ID:
P17-1047
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
506–517
Language:
URL:
https://aclanthology.org/P17-1047
DOI:
10.18653/v1/P17-1047
Bibkey:
Cite (ACL):
David Harwath and James Glass. 2017. Learning Word-Like Units from Joint Audio-Visual Analysis. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 506–517, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Learning Word-Like Units from Joint Audio-Visual Analysis (Harwath & Glass, ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1047.pdf
Video:
 https://aclanthology.org/P17-1047.mp4
Data
Places205