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 …

The hidden Markov models are statistical models used in many real-world applications and
communities. The use of hidden Markov models has become predominant in the last …

Keyword spotting (KWS) is a critical component for enabling speech based user interactions
on smart devices. It requires real-time response and high accuracy for good user …

Our application requires a keyword spotting system with a small memory footprint, low
computational cost, and high precision. To meet these requirements, we propose a simple …

A method include placing a first processor in a sleep operating mode and running a second
processor that is operative to wake the first processor from the sleep operating mode in …

For retrieving keywords from scanned handwritten documents, we present a word spotting
system that is based on character Hidden Markov Models. In an efficient lexicon-free …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

In this paper, we present an unsupervised learning framework to address the problem of
detecting spoken keywords. Without any transcription information, a Gaussian Mixture Model …

Abstract We propose improved Deep Neural Network (DNN) training loss functions for more
accurate single keyword spotting on resource-constrained embedded devices. The loss …

We present a novel approach to query-by-example keyword spotting (KWS) using a long
short-term memory (LSTM) recurrent neural network-based feature extractor. In our …