基于堆叠的自动编码器的语音情感识别,由PSO优化,基于草纤维根优化.
Chi Zeng1, Jialing Li2, Abbas Habibi3,4
1Xinyang Vocational and Technical College, Xinyang, 464000, Henan, China.
Scientific reports
|July 18, 2025
概括
这项研究使用一种新的深度学习方法来增强语音情感识别 (SER). 通过将堆叠的自动编码器与混合优化相结合,它可以从语音信号中识别情绪,从而达到很高的准确性.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 计算语言学 计算语言学
背景情况:
- 语音情感识别 (SER) 具有挑战性,因为人类情感的主观性质.
- 准确的SER在医疗保健,人机交互和社交机器人等领域都有应用.
- 现有的SER模型在有效捕捉微妙的情感线索方面存在局限性.
研究的目的:
- 开发一种创新高效的语音情感识别系统.
- 为了提高从语音信号识别情绪状态的准确性.
- 将深度学习与元启发式优化整合在一起,以提高SER性能.
主要方法:
- 作为核心深度学习模型,采用了堆叠自动编码器 (SAE).
- 该SAE的性能使用混合元启发算法进行了微调,该算法结合了粒子群优化 (PSO) 和草纤维根优化 (GFRO).
- 从语音信号中提取了光谱和音调特征,包括光谱顶峰,,流量和和声比.
主要成果:
- 拟议的混合深度学习模型在语音情感识别方面表现出高准确度.
- 在标准情绪识别数据集上评估表现.
- 该模型的性能超过了一些最先进的方法,包括CNN,SVM,DL,CNN/INCA和VGG-16.
结论:
- 将SAE与PSO-GFRO集成为有效的语音情感识别提供了一种强大的方法.
- 该方法成功地提取了相关特征,以准确识别情绪.
- 这项研究为SER领域的重大进步做出了贡献,并有可能用于现实世界的应用.
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