Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …

We demonstrate that a production-quality keyword-spotting model can be trained on-device
using federated learning and achieve comparable false accept and false reject rates to a …

Modern wake word detection systems usually rely on neural networks for acoustic modeling.
Transformers has recently shown superior performance over LSTM and convolutional …

In this work, we focus on the robustness of keyword spotting (KWS) at various speech
speeds. First, to enable small-footprint KWS, we graft a depthwise separable convolution …

Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an
indispensable component of smart devices. Recently, end-to-end (E2E) methods have be …

Always-on spoken language interfaces, eg personal digital assistants, rely on a wake word
to start processing spoken input. We present novel methods to train a hybrid DNN/HMM …

Wake-up word spotting in noisy environments is a critical task for an excellent user
experience with voice assistants. Unwanted activation of the device is often due to the …

The performance of the keyword spotting (KWS) system based on audio modality, commonly
measured in false alarms and false rejects, degrades significantly under the far field and …

A keyword spotting (KWS) system running on smart devices should accurately detect the
appearances and predict the locations of predefined keywords from audio streams, with …

Keyword spotting and in particular Wake-Up-Word (WUW) detection is a very important task
for voice assistants. A very common issue of voice assistants is that they get easily activated …