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 …

In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, that
allows the users to include a customizable voice interface to their device and that does not …

Current keyword spotting systems are typically trained with a large amount of pre-defined
keywords. Recognizing keywords in an open-vocabulary setting is essential for …

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 …

Time series-based applications such as recognition of handwriting benefit from using Deep
Neural Networks (DNNs) in terms of accuracy and efficiency. Due to strict power and …

This paper proposes a self-learning method to incrementally train (fine-tune) a personalized
Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio …

This paper describes an end-to-end approach to perform keyword spotting with a pre-trained
acoustic model that uses recurrent neural networks and connectionist temporal classification …

Automatic classification of speech commands has revolutionized human computer
interactions in robotic applications. However, employed recognition models usually follow …

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 …