通过修改水循环优化器优化深度Maxout网络来改善人类和机器的情感联系
Jun Zhao1, Yuanyuan Huang2, Mehdi Moattari3,4
1Chengdu University of Information Technology, Chengdu, China. cdzhaojun1229@163.com.
Scientific reports
|November 25, 2025
概括
这项研究引入了一种新的方法,使计算机能够使用深度Maxout网络和水循环算法来识别语音中的人类情绪. 这种方法显著提高了情绪识别的准确性,增强了人机交互.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 语音 情感 识别 语言
背景情况:
- 机器准确识别人类情绪对于自然的人机交互至关重要.
- 现有的语音情感识别方法往往缺乏稳定性和准确性.
- 深度学习和元启发技术为增强情绪检测提供了潜力.
研究的目的:
- 通过整合深度学习和元启发技巧,开发一种新的方法来识别语言中的情绪.
- 优化深度Maxout网络 (DMN) 的性能,以使用修改后的水循环算法 (MWCA) 进行语音情感识别.
- 在基准语音情感数据集上评估拟议模型的有效性.
主要方法:
- 使用深度Maxout网络 (DMN) 作为核心深度学习框架.
- 采用修改后的水循环算法 (MWCA) 来优化DMN架构参数.
- 使用Mel-Frequency Cepstral Coefficients (MFCC) 提取的语音特征用于情感差异化.
主要成果:
- 拟议的模型在Emo-DB数据集上实现了93.1%的平均准确率和92.4%的F1得分.
- 在语音情感识别方面,与基线模型相比,在统计学上有显著的改善 (p < 0.01).
- 通过使用提取的MFCC特征,成功地从语音信号中区分了各种情绪状态.
结论:
- 集成的DMN和MWCA方法为计算机提供了一个强大的工具,用于理解和响应语音中的人类情绪.
- 这项研究推动了情感互动设计领域的发展,导致了更自然和直观的人机通信.
- 开发的模型在基于语音的情感识别系统的准确性和可靠性上有了显著的改进.
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