Wake word system improves smart assistant conversations with context
Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations
Artificial Intelligence
Summary
Wake word detection helps virtual assistants wake up when you say a special word. This paper shows a smarter system that not only listens for the wake word but also understands if you are actually talking to the assistant after that. The authors created a big set of fake conversations with many speakers to teach the system when to pay attention and when not to. Their tests show this method helps the assistant be more accurate and responsive. They also share their code and data so others can build on their work.
What this means in practice
- •For virtual assistant developers: Build wake word detection that understands user intent and excludes unrelated speech after activation for smarter interactions.$Commercial implications: This enables creating smarter voice assistants that improve user experience by reducing false activations and ignoring irrelevant speech, a key feature for commercial products.
- •For call center software engineers: Use synthetic multi-speaker conversation data to train systems that differentiate between addressed commands and background chatter during calls.
Authors
Marcin Sowański, Kacper Leszczyński, Kacper Krzywicki, Krzysztof Wodnicki
Abstract
Wake word detection is a critical component of virtual assistants, serving as the gateway to seamless user interactions. This paper introduces a novel wake-up system that extends traditional direct keyword detection with contextual trigger detection. After an initial wake word activation, the system uses reasoning to distinguish between user commands and unrelated speech, ensuring efficient and context-aware engagement. We present a data generation architecture that produces a 62.3-hour corpus of controllable multi-speaker conversations containing direct invocations, contextual follow-ups, and non-addressed speech. Experimental results demonstrate the effectiveness of the proposed approach across diverse synthetic conversational scenarios. We release the code, dataset and trained models to promote reproducibility and further advancements in intelligent assistant technologies.