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 propose a sub-μW always-ON keyword spotting (μKWS) chip for audio wake-up
systems. It is mainly composed of a neural network (NN) and a feature extraction (FE) circuit …

In this paper, we propose a novel end-to-end user-defined keyword spotting method that
utilizes linguistically corresponding patterns between speech and text sequences. Unlike …

The goal of this work is to automatically determine whether and when a word of interest is
spoken by a talking face, with or without the audio. We propose a zero-shot method suitable …

Deep neural networks have recently become a popular solution to keyword spotting
systems, which enable the control of smart devices via voice. In this paper, we apply neural …

As a widely used speech-triggered interface, deep-learning-based keyword spotting (KWS)
chips require both ultra-low power and high detection accuracy. We propose a sub …

Deep learning (DL) models such as convolutional neural networks (ConvNets) are being
deployed to solve various computer vision and natural language processing tasks at the …

Keyword spotting with a Small Footprint (SF-KWS) has gained popularity in today's
landscape of smart voice-activated devices, smartphones, and IoT applications. This surge …

Assistive robots are tools that people living with upper body disabilities can leverage to
autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control …

Smart audio devices are gated by an always-on lightweight keyword spotting program to
reduce power consumption. It is however challenging to design models that have both high …