This perspective article explores the challenges and potential of using speech as a
biomarker in clinical settings, particularly when constrained by the small clinical datasets …

Training machine learning algorithms for speech applications requires large, labeled
training data sets. This is problematic for clinical applications where obtaining such data is …

Building clinical speech analytics models that will reliably translate in-clinic requires a
realistic characterization of their performance. So, how well do we estimate the accuracy of …

Disease-modifying treatments are currently assessed in neurodegenerative diseases.
Huntington's Disease represents a unique opportunity to design automatic sub-clinical …

Automated vocal biomarkers are becoming increasingly desired by speech pathologists and
neurologists in order to extend current noninvasive measures of speech motor abnormalities …

Objective: As part of a larger study dedicated to identifying speech and language biomarkers
of neurological decline associated with repetitive head injury (RHI) in professional boxers …

The understanding of human communication development throughout the lifetime involves
the characterization of both segmental and suprasegmental parameters. This pilot study …

REU Project: Quantum Machine Learning for Dysarthric Speech Detection Page 1 REU
Project: Quantum Machine Learning for Dysarthric Speech Detection Funded by NSF CISE …

Dementia comes second only to spinal cord injuries in terms of its debilitating effects; from
memory-loss to physical disability. The standard approach to evaluate cognitive conditions …

Muhammad Ali is a former boxer, a legendary athlete, and a person who lived with
Parkinson's disease for several decades. This chapter examines the controversy …