语音情感识别应用到现实世界的医学咨询
Ching-Tzu Huang1,2, Chih-Wei Huang2, Hsuan-Chia Yang1,2,3
1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Studies in health technology and informatics
|January 25, 2024
概括
面具在医生与患者的互动中增加了声音线索的重要性. 这项研究探讨了语音情感识别,发现语音价值对有效沟通至关重要,特别是在医疗环境中.
科学领域:
- 计算语言学计算语言学
- 情感计算是一种情感计算.
- 医学沟通 医学沟通
背景情况:
- 由于COVID-19的流行,人们不得不戴口罩,改变医疗保健行为和医生与患者的沟通动态.
- 面部表情变得不那么可靠地传达同情心,越来越依赖语音线索.
- 语音情感识别 (SER) 是一个不断增长的研究领域,但其在医疗环境中的应用仍未得到充分探索.
研究的目的:
- 调查YAMNet转移学习在中国普通话语语中非语言情感识别方面的有效性.
- 用真实世界皮肤病诊所数据集评估模型的概括能力.
- 评估语音激活和价值在医生与患者互动中的重要性.
主要方法:
- 利用YAMNet转移学习用于语音情感识别.
- 在NTHU-NTUA中文交互式情感库 (NNIME) 上训练并验证了该模型.
- 在来自皮肤病诊所的医生与患者互动的定制数据集上测试了模型的概括性.
主要成果:
- 在NNIME数据集上实现了0.59的激活精度和0.57的价值精度.
- 在医生与患者数据集上,证明了0.24的激活和0.58的价值的验证准确性.
- 表明价值在这种医疗背景下是一种更强大的情绪预测指标.
结论:
- 语音情感识别,特别是语音价值,对于COVID-19后有效的医生与患者沟通至关重要.
- YAMNet模型显示了SER在医疗场景中的潜力,尽管激活预测需要进一步改进.
- 当面部表情被掩盖时,非语言语音线索对于建立关系和确保患者满意度至关重要.
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