Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work, we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs. This will allow interactions in which language about novel tasks and environments is learned from end-users, reducing dependence on textual inputs and potentially mitigating the effects of demographic bias found in widely available speech recognition systems. We leverage recent work in self-supervised speech representation models and show that learned representations of speech can make language grounding systems more inclusive towards specific groups while maintaining or even increasing general performance.
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On the contributions of visual and textual supervision in low-resource semantic speech retrieval
Recent work has shown that speech paired with images can be
used to learn semantically meaningful speech representations
even without any textual supervision. In real-world low-resource
settings, however, we often have access to some transcribed
speech. We study whether and how visual grounding is useful
in the presence of varying amounts of textual supervision. In
particular, we consider the task of semantic speech retrieval in a
low-resource setting. We use a previously studied data set and
task, where models are trained on images with spoken captions
and evaluated on human judgments of semantic relevance. We
propose a multitask learning approach to leverage both visual
and textual modalities, with visual supervision in the form of keyword
probabilities from an external tagger. We find that visual
grounding is helpful even in the presence of textual supervision,
and we analyze this effect over a range of sizes of transcribed
data sets. With ∼5 hours of transcribed speech, we obtain 23%
higher average precision when also using visual supervision.
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- Award ID(s):
- 1816627
- NSF-PAR ID:
- 10108193
- Date Published:
- Journal Name:
- Interspeech 2019
- ISSN:
- 2308-457X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
