基于深度学习的说话人确认方法研究现状及展望OA北大核心CSTPCD

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.