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The spoken language serves as an accessible and efficient interface, enablingnon-experts and disabled users to interact with complex assistant robots.However, accurately grounding language utterances gives a significant challengedue to the acoustic variability in speakers' voices and environmental noise. Inthis work, we propose a novel speech-scene graph grounding network (SGGNet$^2$)that robustly grounds spoken utterances by leveraging the acoustic similaritybetween correctly recognized and misrecognized words obtained from automaticspeech recognition (ASR) systems. To incorporate the acoustic similarity, weextend our previous grounding model, the scene-graph-based grounding network(SGGNet), with the ASR model from NVIDIA NeMo. We accomplish this by feedingthe latent vector of speech pronunciations into the BERT-based groundingnetwork within SGGNet. We evaluate the effectiveness of using latent vectors ofspeech commands in grounding through qualitative and quantitative studies. Wealso demonstrate the capability of SGGNet$^2$ in a speech-based navigation taskusing a real quadruped robot, RBQ-3, from Rainbow Robotics.

SGGNet$^2$: Speech-Scene Graph Grounding Network for Speech-guided Navigation