Standardized tests play a crucial role in the detection of cognitive
impairment. Previous work demonstrated that automatic detection of cognitive
impairment is possible using audio data from a standardized picture description
task. The presented study goes beyond that, evaluating our methods on data
taken from two standardized neuropsychological tests, namely the German SKT and
a German version of the CERAD-NB, and a semi-structured clinical interview
between a patient and a psychologist. For the tests, we focus on speech
recordings of three sub-tests: reading numbers (SKT 3), interference (SKT 7),
and verbal fluency (CERAD-NB 1). We show that acoustic features from
standardized tests can be used to reliably discriminate cognitively impaired
individuals from non-impaired ones. Furthermore, we provide evidence that even
features extracted from random speech samples of the interview can be a
discriminator of cognitive impairment. In our baseline experiments, we use
OpenSMILE features and Support Vector Machine classifiers. In an improved
setup, we show that using wav2vec 2.0 features instead, we can achieve an
accuracy of up to 85%.

该研究使用标准化测试中的语音数据，通过使用 OpenSMILE 特征和支持向量机分类器，成功实现了认知障碍的自动检测，并使用 wav2vec 2.0 特征提取技术进一步提高了识别准确度。