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

作　　者：李建琛 韩纪庆[1] LI Jianchen;HAN Jiqing(School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China)

机构地区：[1]哈尔滨工业大学计算机科学与技术学院,哈尔滨150001

出　　处：《数据采集与处理》2024年第5期1062-1084,共23页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(62376071)。

摘　　要：随着深度学习的不断发展,说话人确认(Speaker verification)技术已经取得了长足的进步。该技术相较于其他生物特征识别技术,具有可远程操作、成本低和易于人机交互等优势,在公安刑侦、金融服务等领域展现出广泛的应用前景。本文系统综述了基于深度学习的说话人确认技术的发展脉络。首先,介绍了基于深度学习的说话人特征表示模型在模型输入与结构、池化层、有监督损失函数和自监督学习与预训练模型4个方面的发展历程和研究现状;其次,探讨了说话人确认技术在实际应用中面临的跨域不匹配问题,如噪声干扰、信道不匹配和远场语音等,并概述了相应的领域自适应和领域泛化方法;最后,指出了进一步的研究方向。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 

分 类 号：TN912[电子电信—通信与信息系统]