Feedback Mechanism for Customer Care Service via Speech Emotion Recognition

Anuraj Singh, Praveen Kumar Sahu, Lakshya Bhardwaj
{"title":"Feedback Mechanism for Customer Care Service via Speech Emotion Recognition","authors":"Anuraj Singh, Praveen Kumar Sahu, Lakshya Bhardwaj","doi":"10.1109/IATMSI56455.2022.10119392","DOIUrl":null,"url":null,"abstract":"Speech Emotion Recognition is not a very active field of research. There are no standard models available. In this paper, we have used explicit feature engineering process instead of using a Neural Network model for feature extraction. Specific data augmentations are applied to generalize the data to a greater extent. A simple and more controllable yet, effective and efficient classifier is created with basic layers. The model contains just over 600K parameters. The accuracy achieved on unseen test data is 84%.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IATMSI56455.2022.10119392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Speech Emotion Recognition is not a very active field of research. There are no standard models available. In this paper, we have used explicit feature engineering process instead of using a Neural Network model for feature extraction. Specific data augmentations are applied to generalize the data to a greater extent. A simple and more controllable yet, effective and efficient classifier is created with basic layers. The model contains just over 600K parameters. The accuracy achieved on unseen test data is 84%.
基于语音情感识别的客户关怀服务反馈机制
语音情感识别并不是一个非常活跃的研究领域。没有可用的标准模型。在本文中,我们使用显式特征工程过程代替神经网络模型进行特征提取。应用特定的数据扩充来更大程度地概括数据。一个简单的,更可控的,有效的和高效的分类器被创建与基本层。该模型包含超过600K个参数。在未见的测试数据上实现的准确率为84%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信