Fine-Grained Grounding for Multimodal Speech Recognition

Tejas Srinivasan, Ramon Sanabria, Florian Metze, Desmond Elliott


Abstract
Multimodal automatic speech recognition systems integrate information from images to improve speech recognition quality, by grounding the speech in the visual context. While visual signals have been shown to be useful for recovering entities that have been masked in the audio, these models should be capable of recovering a broader range of word types. Existing systems rely on global visual features that represent the entire image, but localizing the relevant regions of the image will make it possible to recover a larger set of words, such as adjectives and verbs. In this paper, we propose a model that uses finer-grained visual information from different parts of the image, using automatic object proposals. In experiments on the Flickr8K Audio Captions Corpus, we find that our model improves over approaches that use global visual features, that the proposals enable the model to recover entities and other related words, such as adjectives, and that improvements are due to the model’s ability to localize the correct proposals.
Anthology ID:
2020.findings-emnlp.242
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2667–2677
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.242
DOI:
10.18653/v1/2020.findings-emnlp.242
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.242.pdf