In this work, we present Slimmable Neural Networks applied to the problem of small-footprint
keyword spotting. We show that slimmable neural networks allow us to create super-nets …

Self-supervised speech representation learning (S3RL) is revolutionizing the way we
leverage the ever-growing availability of data. While S3RL related studies typically use large …

Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted
for smart devices. To this extent, neural networks are extensively used for their flexibility and …

Speech interactions and digital assistants rely on effective wake word detection models to
identify predefined words. In this study, we address the need for an efficient wake word …

Voice trigger detection is an important task, which enables activating a voice assistant when
a target user speaks a keyword phrase. A detector is typically trained on speech data …

Imaging underwater environments is crucial to advancing our understanding of marine
organisms, climate change, marine geology, aquaculture farming, and underwater …

Conversational agents commonly utilize keyword spotting (KWS) to initiate voice interaction
with the user. For user experience and privacy considerations, existing approaches to KWS …

During an interaction with a robot, the first thing we usually do is wake up the robot using a
wake word. For example:'**aodu**aodu'and 'Hey, Siri', these wake words undoubtedly …