Investigating the Relationship between Signs of Depression and Alzheimer's Dementia in Speech

Depression and Alzheimer's dementia frequently co-occur, with depression often occurring at early stages of Alzheimer's disease (AD). We investigate whether knowledge of a person's depression score gained through analysis of acoustic features of the person's speech can help detect AD. We analysed data from 239 participants, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=162$</tex> individuals had a clinical diagnosis of AD, to explore correlations between depression scores, cognitive test scores and AD status. First, we created multilayer perceptron models for depression assessment using speech features extracted through our active data representation method and wav2vec features. We then used outputs from the depression models as predictors in our Alzheimer's detection models. Depression scores were strongly correlated with Alzheimer's diagnosis in this data set (Spearman's correlation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r = -0.610$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r = -0.536,\ p &lt; 0.01$</tex> for correlations between depression scores and diagnosis and cognitive test scores, respectively). We found that depression scores could predict AD fairly accurately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(Acc=0.84)$</tex>, and that depression could be detected in speech with moderate accuracy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(Acc=0.79)$</tex>. Using these predictions and acoustic features, our dementia model reached <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Acc=0.70$</tex>. We also investigated classification of AD from speech in the presence and absence of depression, obtaining the same level of accuracy for this imbalanced prediction task.