Smart healthcare has achieved significant progress in recent years. Emerging artificial
intelligence (AI) technologies enable various smart applications across various healthcare …

ABSTRACT Automatic Speech Recognition (ASR) is an active field of research due to its
large number of applications and the proliferation of interfaces or computing devices that …

Consumer speech recognition systems do not work as well for many people with speech
differences, such as stuttering, relative to the rest of the general population. However, what …

Speech datasets are crucial for training Speech Language Technologies (SLT); however,
the lack of diversity of the underlying training data can lead to serious limitations in building …

Speech samples from over 1000 individuals with impaired speech have been submitted for
Project Euphonia, aimed at improving automated speech recognition systems for disordered …

Some tasks in content processing, eg, natural language processing (NLP), like hate or
offensive speech and emotional or funny text detection, are subjective by nature. Each …

Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers
with canonical speech patterns. Unfortunately, these systems perform poorly when tested on …

Automatic recognition of disordered and elderly speech remains a highly challenging task to
date due to the difficulty in collecting such data in large quantities. This paper explores a …

Data practices shape research and practice on fairness in machine learning (fair ML).
Critical data studies offer important reflections and critiques for the responsible …

Self-supervised learning (SSL) based speech foundation models have been applied to a
wide range of ASR tasks. However, their application to dysarthric and elderly speech via …