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Comparison of read and spontaneous speech in case of automatic detection of depression

机译:自动检测抑郁症的读取和自发言论的比较

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In this paper, read and spontaneous speech have been compared in the light of automatic depression detection by speech processing. First, statistical analysis was carried out to select those acoustic features that differ significantly between healthy and depressed subjects in case of these two types of speech, separately for both gender. Secondly, statistical examination and classification experiments were prepared to compare the values of the selected features for the two types of speech. We were looking for the answer to which type of speech can be used to achieve better automatic depression detection results. As it was expected, the tempo related features, such as articulation rate, speech rate, and pause lengths are useful in case of spontaneous speech, while formants trajectories can be used only in case of read speech, because their values are mainly influenced by the linguistic content of the speech. Despite the significant differences of the features' values between read and spontaneous speech, there were no major differences in the detection accuracies. 83% detection accuracy was archived with read speech samples, and 86% detection accuracy was achieved with spontaneous speech samples.
机译:在本文中,通过语音处理的自动抑制检测光,比较了读取和自发的语音。首先,进行统计分析,以选择这些声学特征,在这种两种语音中,对于两种性别分开进行这两种语音。其次,准备统计检查和分类实验以比较两种类型的语音所选特征的值。我们正在寻找可用于实现更好的自动抑郁检测结果的语音的答案。在预期的情况下,在自发性语音的情况下,节奏相关特征,例如铰接率,语音率和暂停长度是有用的,而制造者轨迹只能在读取语音的情况下使用,因为它们的值主要受到影响语言内容的演讲。尽管具有读取和自发性言论之间的特征的价值观存在显着差异,但检测准确性没有重大差异。 83 %检测精度被读取语音样本存档,并且通过自发的语音样本实现了86℃的检测精度。

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