Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge
This work addresses the lack of standardization in Alzheimer's speech research by offering a balanced dataset and tasks for researchers, though it is incremental as it builds on existing methods without introducing new paradigms.
The ADReSS Challenge introduced a standardized benchmark dataset for automated Alzheimer's dementia recognition from spontaneous speech, providing baseline results for classification and regression tasks to facilitate methodological comparisons in the field.
The ADReSS Challenge at INTERSPEECH 2020 defines a shared task through which different approaches to the automated recognition of Alzheimer's dementia based on spontaneous speech can be compared. ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task. In the Alzheimer's speech classification task, ADReSS challenge participants create models for classifying speech as dementia or healthy control speech. In the the neuropsychological score regression task, participants create models to predict mini-mental state examination scores. This paper describes the ADReSS Challenge in detail and presents a baseline for both tasks, including feature extraction procedures and results for classification and regression models. ADReSS aims to provide the speech and language Alzheimer's research community with a platform for comprehensive methodological comparisons. This will hopefully contribute to addressing the lack of standardisation that currently affects the field and shed light on avenues for future research and clinical applicability.