Cross-Lingual Features for Alzheimer’s Dementia Detection from Speech

Alzheimer’s dementia is a neurodegenerative disease that affects millions of people worldwide. Early detection of Alzheimer’s dementia is crucial for effective treatment and management of the disease. In this paper, we present a cross-lingual approach for detecting Alzheimer’s dementia from speech, based on multiple feature streams that capture the individual’s speech and conversational interactions. In order to validate the ability of the features to perform well in cross-linguistic scenarios, we evaluate in a zero-shot setup, where the target domain is a language that was not available during training and a few-shot setup, where only limited data is available. Experimental results show that an ensemble system using the features trained on English and evaluated on Greek outperforms the baseline system by 4.4 %. Further experiments show promising zero-shot and few-shot performance on a similar Spanish task.

阿尔茨海默病性痴呆是一种神经退行性疾病，影响着全球数百万人。阿尔茨海默病性痴呆的早期检测对于该疾病的有效治疗和管理至关重要。在本文中，我们提出了一种从语音中检测阿尔茨海默病性痴呆的跨语言方法，该方法基于捕捉个体语音和对话交互的多个特征流。为了验证这些特征在跨语言场景中的良好性能，我们在零样本设置（目标域是训练期间未涉及的一种语言）和少样本设置（只有有限的数据可用）中进行评估。实验结果表明，一个使用在英语上训练并在希腊语上评估的特征的集成系统比基线系统性能高出4.4%。进一步的实验在一个类似的西班牙语任务上显示出有前景的零样本和少样本性能。