  As technology advances and digital devices become prevalent, seamless
human-machine communication is increasingly gaining significance. The growing
adoption of mobile, wearable, and other Internet of Things (IoT) devices has
changed how we interact with these smart devices, making accurate spoken words
recognition a crucial component for effective interaction. However, building
robust spoken words detection system that can handle novel keywords remains
challenging, especially for low-resource languages with limited training data.
Here, we propose PLiX, a multilingual and plug-and-play keyword spotting system
that leverages few-shot learning to harness massive real-world data and enable
the recognition of unseen spoken words at test-time. Our few-shot deep models
are learned with millions of one-second audio clips across 20 languages,
achieving state-of-the-art performance while being highly efficient. Extensive
evaluations show that PLiX can generalize to novel spoken words given as few as
just one support example and performs well on unseen languages out of the box.
We release models and inference code to serve as a foundation for future
research and voice-enabled user interface development for emerging devices.
