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 …

A key element in solving real-life data science problems is selecting the types of models to
use. Tree ensemble models (such as XGBoost) are usually recommended for classification …

Early diagnosis is critical for the development and success of interventions, and
neuroimaging is one of the most promising areas for early detection of Alzheimer's disease …

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered
serious reliability concerns. A prominent example is hardware transient faults that are …

Driven by the vision of Internet of Things, some research efforts have already focused on
designing a network of efficient speech recognition for the development of edge computing …

Keyword spotting (KWS) is a major component of human-computer interaction for smart on-
device terminals and service robots, the purpose of which is to maximize the detection …

This paper investigates machine learning approaches toward the development of a speaker
dependent keywords spotting system intended for users with speech disorders, in particular …

We study the problem of fast and efficient classification of sequential data (such as time-
series) on tiny devices, which is critical for various IoT related applications like audio …

Always-on keyword spotting (KWS) that detects wake-up words has been the indispensable
module in the voice interaction system. However, the ultra-low-power embedded devices put …

This paper presents the design and development of Signed Carry Disregard Multiplier
(SCDM8), a family of signed approximate multipliers tailored for integration into …