{"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%.