Natural language goal understanding for smart home environments

Abstract

One of the main challenges of the Internet of Things (IoT) is to enable end-users without technical experience to use, control or monitor smart devices. However, enabling end-users to interact with these smart devices in an intuitive and natural way becomes increasingly important as they become more pervasive in our homes, workplaces and public environments. Voice-based interfaces are the emerging trend to provide a more natural human-device interaction in smart environments. Such interfaces require Natural Language Understanding (NLU) approaches to identify the meaning of end-users' voice inputs. Designing voice interfaces that are not limited to a small, fixed set of pre-defined commands is far from trivial. Existing voice-based solutions in the smart home domain either restrict the end-users to follow a strict language pattern, do not support indirect goals, require a large training dataset, or need a voice assistant located in the cloud. In this paper, we propose an approach for understanding end-users goals from voice inputs in smart homes. Our approach alleviates the need for end-users to learn or remember concrete operations of the devices and specific words/pattern structures rather it enables them to control their smart homes based on the desired goals (effects). We evaluate the approach through application to a collection of 253 goals from real end-users and report on quality metrics. The results demonstrate that our solution provides a good accuracy, high precision and acceptable recall for understanding end-users goals in the smart home domain.