  The usage of automatic speech recognition (ASR) systems are becoming
omnipresent ranging from personal assistant to chatbots, home, and industrial
automation systems, etc. Modern robots are also equipped with ASR capabilities
for interacting with humans as speech is the most natural interaction modality.
However, ASR in robots faces additional challenges as compared to a personal
assistant. Being an embodied agent, a robot must recognize the physical
entities around it and therefore reliably recognize the speech containing the
description of such entities. However, current ASR systems are often unable to
do so due to limitations in ASR training, such as generic datasets and
open-vocabulary modeling. Also, adverse conditions during inference, such as
noise, accented, and far-field speech makes the transcription inaccurate. In
this work, we present a method to incorporate a robot's visual information into
an ASR system and improve the recognition of a spoken utterance containing a
visible entity. Specifically, we propose a new decoder biasing technique to
incorporate the visual context while ensuring the ASR output does not degrade
for incorrect context. We achieve a 59% relative reduction in WER from an
unmodified ASR system.
