前奏对精神分裂症机器分类的贡献。

IF 3 Q2 PSYCHIATRY
Tomer Ben Moshe, Ido Ziv, Nachum Dershowitz, Kfir Bar
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引用次数: 0

摘要

我们展示了如何通过计算利用声学前音特征(如音高和间隙)从单个口语应答中检测出精神分裂症的症状。我们比较了声学模态和以前使用的文本模态对算法判断说话者是否患有精神分裂症的各自贡献。我们的分类结果清楚地表明,我们能比文字模式更好地提取相关的声学特征。我们发现,当与这些声学特征相结合时,文本特征只能稍微改善分类效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The contribution of prosody to machine classification of schizophrenia.

The contribution of prosody to machine classification of schizophrenia.

We show how acoustic prosodic features, such as pitch and gaps, can be used computationally for detecting symptoms of schizophrenia from a single spoken response. We compare the individual contributions of acoustic and previously-employed text modalities to the algorithmic determination whether the speaker has schizophrenia. Our classification results clearly show that we can extract relevant acoustic features better than those textual ones. We find that, when combined with those acoustic features, textual features improve classification only slightly.

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