论文标题
阿尔茨海默氏症的痴呆症通过自发的演讲识别：《地址挑战》
Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge
论文作者
论文摘要
Interspeech 2020的地址挑战定义了一项共同的任务，可以比较基于自发语音的阿尔茨海默氏痴呆症对自动识别的不同方法。 Adress为研究人员提供了一个基准的语音数据集，该数据集已在年龄和性别方面进行了审美的预处理和平衡，定义了两个认知评估任务，即：阿尔茨海默氏症的语音分类任务和神经心理评分回归任务。在阿尔茨海默氏症的语音分类任务中，Adress挑战参与者创建了将语音归类为痴呆或健康控制语音的模型。在神经心理评分回归任务中，参与者创建了模型来预测迷你的状态考试分数。本文详细介绍了地址挑战，并为这两个任务提供了基线，包括特征提取程序和分类和回归模型的结果。 Adress旨在为阿尔茨海默氏症的言语和语言研究社区提供一个全面的方法论比较的平台。希望这将有助于解决目前影响该领域的缺乏标准化，并阐明未来的研究和临床适用性的途径。
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.
