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基于语音的抑郁评估系统的性能评估,考虑输入话语的数量和类型。

Performance Evaluation of a Voice-Based Depression Assessment System Considering the Number and Type of Input Utterances.

机构信息

Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.

Mitsui Knowledge Industry Co., Ltd., Tokyo 105-6215, Japan.

出版信息

Sensors (Basel). 2021 Dec 23;22(1):67. doi: 10.3390/s22010067.

Abstract

It is empirically known that mood changes affect facial expressions and voices. In this study, the authors have focused on the voice to develop a method for estimating depression in individuals from their voices. A short input voice is ideal for applying the proposed method to a wide range of applications. Therefore, we evaluated this method using multiple input utterances while assuming a unit utterance input. The experimental results revealed that depressive states could be estimated with sufficient accuracy using the smallest number of utterances when positive utterances were included in three to four input utterances.

摘要

经验表明,情绪变化会影响面部表情和声音。在这项研究中,作者专注于声音,旨在开发一种通过声音来评估个体抑郁程度的方法。对于将所提出的方法应用于广泛的应用场景来说,简短的输入声音是非常理想的。因此,我们在假设单元话语输入的情况下,使用多个输入话语来评估该方法。实验结果表明,当在三到四个输入话语中包含积极话语时,使用最少数量的话语就可以足够准确地估计出抑郁状态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5108/8747535/b5ae2a6128a8/sensors-22-00067-g001.jpg

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