{"title":"Addressing data scarcity in speech emotion recognition: A comprehensive review","authors":"Samuel Kakuba , Dong Seog Han","doi":"10.1016/j.icte.2024.11.003","DOIUrl":null,"url":null,"abstract":"<div><div>Speech emotion recognition (SER) is a critical field within affective computing, aiming to detect and classify emotional states from speech signals, which vary dynamically over time. These signals encode complex relationships between features at multiple time scales, effectively reflecting a speaker’s emotional state. Despite significant progress, SER faces the persistent challenge of labeled data scarcity, a major obstacle given the data-intensive requirements of deep learning models. This scarcity often results in small, imbalanced datasets that hinder model generalization. Various strategies, including feature selection, data augmentation, domain adaptation, and fusion techniques, have been employed to mitigate these issues. However, comprehensive reviews that critically analyze these methods remain limited. In this paper, we provide an extensive review of these data scarcity strategies in SER, assessing their merits and limitations in terms of efficiency and robustness. Special attention is given to how these strategies enhance the performance of both acoustic and multimodal SER systems when operating on limited datasets. Additionally, we highlight the potential of fusion strategies combined with attention mechanisms as promising solutions to improve convergence and reduce model complexity.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 1","pages":"Pages 110-123"},"PeriodicalIF":4.1000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICT Express","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405959524001413","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Speech emotion recognition (SER) is a critical field within affective computing, aiming to detect and classify emotional states from speech signals, which vary dynamically over time. These signals encode complex relationships between features at multiple time scales, effectively reflecting a speaker’s emotional state. Despite significant progress, SER faces the persistent challenge of labeled data scarcity, a major obstacle given the data-intensive requirements of deep learning models. This scarcity often results in small, imbalanced datasets that hinder model generalization. Various strategies, including feature selection, data augmentation, domain adaptation, and fusion techniques, have been employed to mitigate these issues. However, comprehensive reviews that critically analyze these methods remain limited. In this paper, we provide an extensive review of these data scarcity strategies in SER, assessing their merits and limitations in terms of efficiency and robustness. Special attention is given to how these strategies enhance the performance of both acoustic and multimodal SER systems when operating on limited datasets. Additionally, we highlight the potential of fusion strategies combined with attention mechanisms as promising solutions to improve convergence and reduce model complexity.
期刊介绍:
The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.