Cognitive and Acoustic Speech and Language Patterns Occurring in Different Neurodegenerative Disorders while Performing Neuropsychological Tests

M. Iglesias, A. Favaro, C. Motley, E. Oh, R. Stevens, A. Butala, L. Moro-Velázquez, N. Dehak
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引用次数: 2

Abstract

In the last decade, improvements in automated speech processing, powered by signal processing and machine learning, has led to new approaches for medical assessment. Additionally, previous research in clinical speech has identified interpretable measures that are sensitive to changes in the cognitive, linguistic, affective, and motoric domains. In order to include speech-based automatic approaches in clinical applications, factors such as robustness, specificity, and interpretability of speech features are crucial. We focused on the analysis of a multi-modal array of interpretable features obtained from the spoken responses of participants with Neurodegenerative Diseases (ND) and control participants (CN) to neuropsychological tests. ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). We first collected spoken responses to three tests, a modified version of the Stroop test (MST), a verb naming task (VNT), and a noun naming task (NNT). Then, we arranged two complementary sets of cognitive and acoustic features and analyzed their statistical significance between the groups studied. Our results suggested that AD participants had significantly greater reaction times and significantly lower response accuracy with respect to the other groups across tests. In addition, PDM participants, compared to CN and PD participants, took a significantly longer time to complete the MST and NNT, while all the groups of participants with NDs showed significantly lower confidence during the MST. Since the analyzed features provided good differentiation results, they can be used in diagnostic tools for the assessment of NDs.
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在进行神经心理学测试时,不同神经退行性疾病中发生的认知和听觉语音和语言模式
在过去的十年里,在信号处理和机器学习的推动下,自动语音处理的改进为医学评估带来了新的方法。此外,先前的临床言语研究已经确定了对认知、语言、情感和运动领域的变化敏感的可解释测量。为了将基于语音的自动方法纳入临床应用,语音特征的鲁棒性、特异性和可解释性等因素至关重要。我们重点分析了神经退行性疾病(ND)和对照参与者(CN)对神经心理测试的口头反应中获得的多模态可解释特征。ND参与者患有阿尔茨海默病(AD)、帕金森氏病(PD)或帕金森氏病模拟(PDM)。我们首先收集了对三个测试的口头回答,一个是修改版的Stroop测试(MST),一个是动词命名任务(VNT),一个是名词命名任务(NNT)。然后,我们安排了两组互补的认知和声学特征,并分析了它们在研究组之间的统计学意义。我们的研究结果表明,与其他组相比,AD参与者的反应时间明显更长,反应准确性明显较低。此外,与CN和PD参与者相比,PDM参与者完成MST和NNT所需的时间明显更长,而所有NDs参与者组在MST期间都表现出明显较低的信心。由于分析的特征提供了良好的区分结果,因此它们可以用于评估NDs的诊断工具。
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