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.

该研究利用信号处理和机器学习方法基于自然语言数据构建预测模型，旨在探究多语言环境下应用于阿尔茨海默病检测的声学特征表达方法，其基准系统在语音阿尔茨海默病检测方面准确率达到 73.91%、在认知分数预测方面根均方误差为 4.95。