论文标题
结合韵律，语音质量和词汇特征，以自动检测阿尔茨海默氏病
Combining Prosodic, Voice Quality and Lexical Features to Automatically Detect Alzheimer's Disease
论文作者
论文摘要
如今，阿尔茨海默氏病（AD）是最常见的痴呆形式，其自动检测可以帮助在早期识别症状，因此可以采取预防措施。此外，基于口语数据的非侵入性技术对于开发AD自动检测系统至关重要。从这个角度来看，本文是对地址挑战的贡献，目的是改善自动言语的AD自动检测。为此，将108名参与者的录音（年龄，性别和AD状况平衡）用作培训设置，以执行两个不同的任务：分类为AD/非AD AD条件，以及在迷你期间州考试（MMSE）分数中的回归。这两个任务均已根据韵律和语音质量从语音中提取28个功能，以及基于词汇和转弯信息的转录中的51个功能。我们的结果使用随机森林分类器实现了高达87.5％的分类精度，而在提供的测试集中，使用带有随机梯度下降的线性回归进行了4.54 RMSE。这表明了通过语音和词汇特征自动检测阿尔茨海默氏病的结果。
Alzheimer's Disease (AD) is nowadays the most common form of dementia, and its automatic detection can help to identify symptoms at early stages, so that preventive actions can be carried out. Moreover, non-intrusive techniques based on spoken data are crucial for the development of AD automatic detection systems. In this light, this paper is presented as a contribution to the ADReSS Challenge, aiming at improving AD automatic detection from spontaneous speech. To this end, recordings from 108 participants, which are age-, gender-, and AD condition-balanced, have been used as training set to perform two different tasks: classification into AD/non-AD conditions, and regression over the Mini-Mental State Examination (MMSE) scores. Both tasks have been performed extracting 28 features from speech -- based on prosody and voice quality -- and 51 features from the transcriptions -- based on lexical and turn-taking information. Our results achieved up to 87.5 % of classification accuracy using a Random Forest classifier, and 4.54 of RMSE using a linear regression with stochastic gradient descent over the provided test set. This shows promising results in the automatic detection of Alzheimer's Disease through speech and lexical features.
