DyCoDe: Dynamic Context Detection from Voice Commands and Conversations in Smart Homes

As voice assistants become integral to smart home environments, the need for accurate context interpretation of voice commands is critical for ensuring seamless and autonomous device control. Traditional smart assistants like Siri, Amazon Alexa, and Google Home often depend on users remembering specific commands, which can limit the user experience. Hence, this paper proposes a novel dynamic context detection framework, DyCoDe, that aims to eliminate the need to remember pre-defined commands. Additionally, with the advancement of technology, newly invented appliances and sensors are being employed in IoT smart home environments, creating a diverse and unique nature for each smart home. Our framework incorporates descriptions and capabilities of these new appliances to ensure accurate context detection. DyCoDe continuously processes conversational data, employing techniques such as clustering, topic identification, and zero-shot learning to detect and adapt to new contexts automatically. This adaptive model evolves alongside its environment, identifying and enhancing autonomous actuation without requiring manual intervention. Our dynamic context detection framework builds on our prior context recognition model, which combines a transformer architecture, attention mechanism, and fully connected neural network. We have extended this model to offer an unsupervised and adaptive detection framework for diverse smart home environments. Our evaluation results show that DyCoDe significantly improves context detection in smart homes, allowing voice assistants to automate a broader range of daily tasks more effectively.