Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge
This addresses the societal and medical need for non-invasive AD detection, but it is incremental as it applies existing methods to a new multilingual context.
The paper tackled the problem of detecting Alzheimer's Dementia (AD) using spontaneous speech data across languages, achieving an accuracy of 73.91% on AD detection and a root mean squared error of 4.95 on cognitive score prediction.
This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD). Participants were invited to employ signal processing and machine learning methods to create predictive models based on spontaneous speech data. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek). To the best of our knowledge no work has investigated acoustic features of the speech signal in multilingual AD detection. Our baseline system used conventional machine learning algorithms with Active Data Representation of acoustic features, achieving accuracy of 73.91% on AD detection, and 4.95 root mean squared error on cognitive score prediction.