在患痴呆症高风险的人群中,语音情感识别
Dongseon Kim1, Bongwon Yi2, Yugwon Won3
1Department of Silver Business, Sookmyung Women's University, Seoul, Korea.
Dementia and neurocognitive disorders
|August 8, 2024
概括
这项研究开发了语音情绪识别 (SER) 技术,以对患有痴呆症高风险的个体进行情绪分类,使用两阶段算法达到70%的准确性. 这一进步有助于预防老年痴呆和护理规划.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 老年学是指老年学的学科.
背景情况:
- 了解痴呆症患者的情绪对于有效的护理和干预至关重要.
- 可以利用现有的技术来满足痴呆症患者的情感需求.
研究的目的:
- 开发语音情感识别 (SER) 技术,用于对患有痴呆症高风险的个体进行情感分类.
- 利用SER用于痴呆症预防,早期干预和护理规划.
主要方法:
- 来自患有痴呆症高风险的个体的语音样本被分析为情感内容.
- 为自动化SER开发了一个结合CNN,LSTM,注意层和Wav2Vec2的深度学习模型.
- 实现了一种两阶段算法,结合了基于文本和基于语音的分析.
主要成果:
- 鉴定了27种不同的情绪,并将其分类为6种详细的情绪状态和3种基本的情绪状态.
- 两个阶段的算法在情绪分类中达到70%的最高准确度.
- 参与者特征和数据收集方法,特别是与伴侣机器人的互动,影响了表现.
结论:
- 这项研究强调了痴呆风险较高的人群中情绪的复杂性.
- 开发的SER算法显示出潜力,但需要进一步改进.
- 为未来的研究,建议对痴呆症患者进行全面和系统地构建的数据集.
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