Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech
This work addresses the challenge of making language grounding systems more inclusive and reducing demographic bias for end users in interactive learning environments.
The paper tackles the problem of grounded language acquisition by using raw speech inputs paired with visual percepts instead of relying on textual inputs, demonstrating feasibility and showing that learned speech representations can maintain or increase general performance while being more inclusive towards specific groups.
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.