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 …

Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

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 …

Energy-harvesting technology provides a promising platform for future IoT applications.
However, since communication is very expensive in these devices, applications will require …

Deep model compression has been extensively studied, and state-of-the-art methods can
now achieve high compression ratios with minimal accuracy loss. This paper studies model …

We describe a large vocabulary speech recognition system that is accurate, has low latency,
and yet has a small enough memory and computational footprint to run faster than real-time …

In this paper we investigate a time delay neural network (TDNN) for a keyword spotting task
that requires low CPU, memory and latency. The TDNN is trained with transfer learning and …

We propose a max-pooling based loss function for training Long Short-Term Memory
(LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and …

High computational complexity hinders the widespread usage of neural networks, especially
in mobile devices, which are often the basis of fine-grained localization technology for …

We study the problem of compressing recurrent neural networks (RNNs). In particular, we
focus on the compression of RNN acoustic models, which are motivated by the goal of …