检索结果-南通市图书馆

1D-CNN:Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features

Computers, Materials & Continua 2021年第6期67卷 4039-4059页

作者： mustaqeem Soonil Kwon Interaction Technology Laboratory Department of SoftwareSejong UniversitySeoul05006Korea

Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call ***,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not *** main aim is to develop a new technique that increases the recognition rate with reasonable cost *** this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called *** HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation ***,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal *** final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the *** proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively.

关键词： Affective computing one-dimensional dilated convolutional neural network emotion recognition gated recurrent unit raw audio clips

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

TC-Net:A Modest&Lightweight Emotion Recognition System Using Temporal Convolution Network

引用

Computer Systems Science & Engineering 2023年第9期46卷 3355-3369页

作者： Muhammad Ishaq mustaqeem Khan Soonil Kwon Sejong University Software Convergence Department Seoul05006Korea Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI) Abu Dhabi3838-111188United Arab Emirates

Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and *** Emotion Recognition(SER)is one of the critical sources for human evaluation,which is applicable in many real-world applications such as healthcare,call centers,robotics,safety,and virtual *** work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional *** authors designed a Temporal Convolutional Network(TCN)core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network to fuse the spatial features and recognize global information for final *** proposed network extracts valid sequential cues automatically from speech signals,which performed better than state-of-the-art(SOTA)and traditional machine learning *** of the proposed method show a high recognition rate compared with *** final unweighted accuracy of 80.84%,and 92.31%,for interactive emotional dyadic motion captures(IEMOCAP)and berlin emotional dataset(EMO-DB),indicate the robustness and efficiency of the designed model.

关键词： Affective computing deep learning emotion recognition speech signal temporal convolutional network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：