  Speech pause is an effective biomarker in dementia detection. Recent deep
learning models have exploited speech pauses to achieve highly accurate
dementia detection, but have not exploited the interpretability of speech
pauses, i.e., what and how positions and lengths of speech pauses affect the
result of dementia detection. In this paper, we will study the positions and
lengths of dementia-sensitive pauses using adversarial learning approaches.
Specifically, we first utilize an adversarial attack approach by adding the
perturbation to the speech pauses of the testing samples, aiming to reduce the
confidence levels of the detection model. Then, we apply an adversarial
training approach to evaluate the impact of the perturbation in training
samples on the detection model. We examine the interpretability from the
perspectives of model accuracy, pause context, and pause length. We found that
some pauses are more sensitive to dementia than other pauses from the model's
perspective, e.g., speech pauses near to the verb "is". Increasing lengths of
sensitive pauses or adding sensitive pauses leads the model inference to
Alzheimer's Disease, while decreasing the lengths of sensitive pauses or
deleting sensitive pauses leads to non-AD.
