计算机工程2025，Vol.51Issue(4)：97-106,10.DOI:10.19678/j.issn.1000-3428.0069185
基于改进高效通道注意力机制的多特征语音情感识别
Multi-Feature Speech Emotion Recognition Based on Improved Efficient Channel Attention Mechanism
摘要
Abstract
The attention mechanism has been widely employed in the field of Speech Emotion Recognition(SER).However,traditional attention modules,while enhancing model performance,also significantly increase the model parameter count.Although the Efficient Channel Attention(ECA)mechanism has a small number of parameters,it can only generate attention weights for the channel dimension.In response to this challenge,an Improved ECA(IECA)module is proposed.IECA module generates corresponding weights for various dimensions of input feature maps with a relatively small number of parameters,enabling the model to more effectively focus on and utilize crucial information within the feature maps.Additionally,to further enhance recognition rates,spectrogram and IS10 features are separately extracted from the speech data.Employing a fusion network,predictions from different branches are combined to yield the final prediction.The proposed model obtained Weighted Accuracy(WA)of 91.63％and 92.46％and Unweighted Average Recall(UAR)of 91.25％and 92.33％on EMODB and CASIA datasets,respectively,which are higher by 2.69-8.43 percentage points and 4.16-10.69 percentage points,respectively,than those reported in previous research.关键词
深度学习/语音情感识别/注意力机制/多特征融合/决策级融合Key words
deep learning/Speech Emotion Recognition(SER)/attention mechanism/multi-feature fusion/decision level fusion分类
计算机与自动化引用本文复制引用
杜晨阳,张雪英,黄丽霞,李娟..基于改进高效通道注意力机制的多特征语音情感识别[J].计算机工程,2025,51(4):97-106,10.基金项目
国家自然科学基金(62271342). （62271342）
