Factors Affecting the Performance of Automated Speaker Verification in
  Alzheimer's Disease Clinical Trials
Malikeh Ehghaghi Marija Stanojevic Ali Akram Jekaterina Novikova
Detecting duplicate patient participation in clinical trials is a major
challenge because repeated patients can undermine the credibility and accuracy
of the trial's findings and result in significant health and financial risks.
Developing accurate automated speaker verification (ASV) models is crucial to
verify the identity of enrolled individuals and remove duplicates, but the size
and quality of data influence ASV performance. However, there has been limited
investigation into the factors that can affect ASV capabilities in clinical
environments. In this paper, we bridge the gap by conducting analysis of how
participant demographic characteristics, audio quality criteria, and severity
level of Alzheimer's disease (AD) impact the performance of ASV utilizing a
dataset of speech recordings from 659 participants with varying levels of AD,
obtained through multiple speech tasks. Our results indicate that ASV
performance: 1) is slightly better on male speakers than on female speakers; 2)
degrades for individuals who are above 70 years old; 3) is comparatively better
for non-native English speakers than for native English speakers; 4) is
negatively affected by clinician interference, noisy background, and unclear
participant speech; 5) tends to decrease with an increase in the severity level
of AD. Our study finds that voice biometrics raise fairness concerns as certain
subgroups exhibit different ASV performances owing to their inherent voice
characteristics. Moreover, the performance of ASV is influenced by the quality
of speech recordings, which underscores the importance of improving the data
collection settings in clinical trials.