Problem
Research questions and friction points this paper is trying to address.
Wake word detection
Contextual trigger detection
Intelligent voice assistant
Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations
🤖 AI Summary
本文提出了一种新的唤醒系统，通过生成可控的多说话人对话数据集来提高虚拟助手在复杂对话场景中的唤醒准确性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
contextual trigger detection
controllable synthetic conversations
data generation architecture
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