数据采集与处理2024，Vol.39Issue(5)：1062-1084,23.DOI:10.16337/j.1004-9037.2024.05.003
基于深度学习的说话人确认方法研究现状及展望
State of the Art and Prospects of Deep Learning-Based Speaker Verification
摘要
Abstract
With the development of deep learning,speaker verification has made great progress.Compared with other biometric identification technologies,this technology has advantages of remote operation,low cost,easy human-computer interaction,etc.,thus it shows a wide range of application prospects in the fields of public security,criminal investigation,and financial services.A systematic overview of the development lineage of deep learning-based speaker verification techniques is provided.Firstly,the development history and research status of deep learning-based speaker representation model are introduced in four aspects:Model input and structure,pooling layer,supervised loss function,and self-supervised learning and pre-training model.Then,the challenges faced by speaker verification are discussed,such as cross-domain mismatch problems like noise interference,channel mismatch and far-field speech,and the corresponding domain adaptation and domain generalization methods are outlined.Finally,the further research directions are presented.关键词
说话人识别/说话人确认/深度学习/领域不匹配/自监督学习Key words
speaker recognition/speaker verification/deep learning/domain mismatch/self-supervised learning分类
信息技术与安全科学引用本文复制引用
李建琛,韩纪庆..基于深度学习的说话人确认方法研究现状及展望[J].数据采集与处理,2024,39(5):1062-1084,23.基金项目
国家自然科学基金(62376071). （62376071）
