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Enhancing Speech-Based Depression Detection Through Gender Dependent Vowel-Level Formant Features

机译:通过基于性别的元音级共振峰特征增强基于语音的抑郁症检测

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Depression has been consistently linked with alterations in speech motor control characterised by changes in formant dynamics. However, potential differences in the manifestation of depression between male and female speech have not been fully realised or explored. This paper considers speech-based depression classification using gender dependant features and classifiers. Presented key observations reveal gender differences in the effect of depression on vowel-level formant features. Considering this observation, we also show that a small set of handcrafted gender dependent formant features can outperform acoustic-only based features (on two state-of-the-art acoustic features sets) when performing two-class (depressed and non-depressed) classification.
机译:抑郁症一直伴随着以共振峰动力学变化为特征的语音运动控制改变。但是,尚未完全实现或探究男女语音之间在抑郁表现中的潜在差异。本文考虑使用基于性别的特征和分类器对基于语音的抑郁症进行分类。提出的主要观察结果揭示了在抑郁对元音级共振峰特征的影响中存在性别差异。考虑到这一观察结果,我们还表明,当执行两类(压抑和非压抑)时,一小套手工制作的性别依赖性共振峰特征可以优于仅基于声学的特征(在两个最新的声学特征集上)分类。

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