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Effective Speech Features for Distinguishing Mild Dementia Patients from Healthy Person

机译:区分健康人的轻度痴呆患者的有效语言特征

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The questionnaire method is generally used for present dementia screening. However, this method requires time for 10 to 15 min with a doctor and a clinical psychologist, which puts a burden on hospitals and test subjects. The purpose of this study is to reduce the burden of users by constructing a system to distinguish patients with mild dementia and healthy persons from speech data. Before that this paper examines the effectiveness of speech features. MFCC has been confirmed to be effective in previous research, this paper extracted six kinds of other speech features that are likely to be correlated with symptoms of dementia. This paper got about 90% accuracy rate for a sentence of conversational speech in SVM and Random Forest. Moreover, this paper has calculated the importance of the features by using the SVM-RFE method. As a result, this showed that log-mel spectrum was more important than MFCC.
机译:调查问卷方法通常用于现有痴呆筛查。 然而,这种方法需要使用医生和临床心理学家的时间10至15分钟,这对医院和测试科目进行了负担。 本研究的目的是通过构建一个系统来减少用户的负担,以区分患有轻度痴呆和健康人的语音数据的患者。 在此之前,审查了语音特征的有效性。 MFCC已被证实在先前的研究中有效,本文提取了六种可能与痴呆症症状相关的其他语音特征。 本文在SVM和随机林中进行了会话演讲的准确度大约90%。 此外,本文通过使用SVM-RFE方法计算了特征的重要性。 结果,这表明Log-Mel光谱比MFCC更重要。

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