检索结果-南通市图书馆

A novel convolutional neural network with gated recurrent unit for automated speech emotion recognition and classification

引用

Journal of Control and Decision 2023年第1期10卷 54-63页

作者： P.Ravi Prakash D.Anuradha Javid Iqbal mohammad gouse galety Ruby Singh S.Neelakandan Department of IT Prasad V Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Business Systems Panimalar Engineering CollegeChennaiIndia Institute of Computer Science and Digital Technology(ICSDI) UCSI UniversityKuala LumpurMalaysia Department of Information Technology College of IT&CSCatholic University in ErbilErbilIraq Department of CSE SRM Institute of Science and TechnologyGhaziabadUttar PradeshIndia Department of CSE R.M.K Engineering CollegeSriperumbudurIndia

Automated Speech Emotion Recognition (SER) becomes more popular and has increased *** concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly depends upon the in-depth analysis of the speech signal,extracts features containing emotional details from the speech signal, and utilises patternrecognition techniques for emotional state identification. The major problem in automatic SERis to extract discriminate, powerful, and emotional salient features from the acoustical content ofspeech signals. The proposed model aims to detect and classify three emotional states of speechsuch as happy, neutral, and sad. The presented model makes use of Convolution neural network– Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set offeature vectors. A comprehensive simulation takes place using the Berlin German Database andSJTU Chinese Database which comprises numerous audio files under a collection of differentemotion labels.

关键词： Emotion recognition speech recognition deep learning classification model Berlin emotion dataset

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

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

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：