The models were initially trained on data from a non-verbal working memory game (1- and 2-back) in the thymia dataset, encompassing over 6,000 participants from the United Kingdom, United States, Mexico, Spain, and Indonesia.

The paper proposes a novel benchmarking approach that helps highlight and quantify these biases in a dataset in the presence of confounders. This underscores the importance of considering demographic factors such as age, sex, and accent in developing and evaluating speech-based diagnostic tools to ensure more accurate and fair health assessments.

The research found that speech characteristics could reliably indicate sleep deprivation and feelings of sleepiness from a single speech activity, in models which properly accounted for individual differences in age, sex, and language/accent. Notably, the ability to predict physical and mental exhaustion improved when analysing data from the same individuals over time.

The study utilised a dataset of 39 video recordings, capturing extensive sessions where clinicians administered, among other things, four cognitive assessment tests. From the first 40 minutes of each clinical session, covering the administration of the Wechsler Intelligence Scale for Children (WISC-5), we extracted head positions and speech turns of both clinician and child.

In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.

This paper presents strong evidence that embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range of different health conditions, including major depressive disorders (MDD).