Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech

Autor:	Kebe, Gaoussou Youssouf, Richards, Luke E., Raff, Edward, Ferraro, Francis, Matuszek, Cynthia
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: To appear in the Proceedings of the 36th AAAI Conference on Artificial Intelligence. February 2022, Vancouver
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.13758 Zobrazit plný text záznamu View this record from Arxiv