Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?
This addresses the challenge of accurate speech recognition for embodied agents like robots in noisy or adverse conditions, though it is an incremental improvement focused on domain-specific applications.
The paper tackled the problem of improving automatic speech recognition (ASR) for robots by incorporating visual context to better recognize speech describing visible entities, achieving a 59% relative reduction in word error rate (WER) compared to an unmodified ASR system.
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.