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 …

ABSTRACT Automatic Speech Recognition (ASR) is an active field of research due to its
large number of applications and the proliferation of interfaces or computing devices that …

We introduce a few-shot transfer learning method for keyword spotting in any language.
Leveraging open speech corpora in nine languages, we automate the extraction of a large …

Open-vocabulary keyword spotting (KWS) aims to detect arbitrary keywords from continuous
speech, which allows users to define their personal keywords. In this paper, we propose a …

Customized keyword spotting (KWS) has great potential to be deployed on edge devices to
achieve hands-free user experience. However, in real applications, false alarm (FA) would …

Open-vocabulary keyword spotting (KWS), which allows users to customize keywords, has
attracted increasingly more interest. However, existing methods based on acoustic models …

Learning to recognize new keywords with just a few examples is essential for personalizing
keyword spotting (KWS) models to a user's choice of keywords. However, modern KWS …

Always-on keyword spotting (KWS), ie, wake word detection, has been widely used in many
voice assistant applications running on smart devices. Although fixed wakeup word …

The advent of Deep-Learning techniques and the increasing importance of personalization
in voice assistants fueled the need for open vocabulary keyword detection systems, in …

Conventional keyword search systems operate on automatic speech recognition (ASR)
outputs, which causes them to have a complex indexing and search pipeline. This has led to …