具有互补特征学习框架和注意力特征融合模块的语音情感识别模型

黄佩瑶; 程慧慧; 唐小煜

doi:10.13568/j.cnki.651094.651316.2023.07.05.0002

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具有互补特征学习框架和注意力特征融合模块的语音情感识别模型

信息科学与技术·人工智能专题 | 更新时间：2026-01-21

- 具有互补特征学习框架和注意力特征融合模块的语音情感识别模型
- 具有互补特征学习框架和注意力特征融合模块的语音情感识别模型
- 新疆大学学报（自然科学版中英文） 2024年41卷第1期页码：52-58
- 作者机构：
  
  1. 华南师范大学工学部电子与信息工程学院
  2. 华南师范大学物理学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金“基于深度学习和能量采集的无线体域网高能效技术研究”（62001173）;广东省大学生科技创新人才培养专项资金项目“基于深度学习的水下目标检测系统”（pdjh2022a0131）
- DOI：10.13568/j.cnki.651094.651316.2023.07.05.0002
  中图分类号： TN912.34;TP18
- 纸质出版：2024
- 稿件说明：
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[1]黄佩瑶,程慧慧,唐小煜.具有互补特征学习框架和注意力特征融合模块的语音情感识别模型[J].新疆大学学报(自然科学版)(中英文),2024,41(01):52-58.
[1]黄佩瑶,程慧慧,唐小煜.具有互补特征学习框架和注意力特征融合模块的语音情感识别模型[J].新疆大学学报(自然科学版)(中英文),2024,41(01):52-58. DOI： 10.13568/j.cnki.651094.651316.2023.07.05.0002.

DOI：10.13568/j.cnki.651094.651316.2023.07.05.0002.

摘要

针对深度学习的特征提取方法无法全面提取语音中的情感特征，也无法有效地融合这些特征的问题，提出了一种集成互补特征学习框架和注意力特征融合模块的语音情感识别模型．该互补特征学习框架包含两条独立的表征提取分支和一条交互互补表征提取分支，能够全面覆盖情感特征的独立性表征和互补性表征．为了进一步优化模型性能，引入注意力特征融合模块，该模块能够根据不同表征对情感分类的贡献程度分配合适的权重，使模型能最大程度地关注对情感识别最有助的特征．基于两个公开情感数据库（Emo-DB和IEMOCAP）的仿真实验结果，验证了所提模型的鲁棒性和有效性．

Abstract

Addressing the limitations of deep learning feature extraction methods

which fail to comprehensively extract and effectively integrate emotional features from speech

this paper proposes a novel speech emotion recognition model. It integrates a complementary feature learning framework and an attention feature fusion module.The complementary feature learning framework consists of two independent representational extraction branches and an interactive complementary representational extraction branch

thoroughly covering both independent and complementary representations of emotional features. To further optimize model performance

an attention feature fusion module is introduced. This module allocates appropriate weights based on the contribution level of different representations to emotion classification

enabling the model to focus maximally on features most beneficial for emotion recognition. Simulation experiments conducted on two public emotion databases(Emo-DB and IEMOCAP) validate the robustness and effectiveness of the proposed model.

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