说话人建模及其应用综述：从深度说话人表示学习视角出发
Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning
AI总结：
本综述从理论与实践双视角梳理深度说话人表示学习的神经方法，涵盖编码器演进、可解释性、鲁棒性方案及开源工具，为说话人建模研究与下游应用提供参考。
摘要：
说话人个性信息是语音信号中最关键的要素之一。通过全面、准确地对该信息建模，可将其应用于多种智能语音场景，例如说话人识别、说话人日志（speaker diarization）、语音合成以及目标说话人提取。本综述从理论与实践两个维度，对说话人表示学习的神经网络方法展开全面梳理。理论层面，我们探讨了从监督学习到自监督学习算法的说话人编码器、从独立模型到大型预训练模型的演进、从纯说话人嵌入学习到与下游任务联合优化的路径，以及可解释性方向的相关研究。实践层面，我们系统分析了提升鲁棒性与有效性的各类方法，并介绍、对比了该领域的多款开源工具包。通过对相关文献、研究活动与资源的系统全面梳理，我们为说话人表征与建模领域的研究者，以及希望将说话人建模技术应用于特定下游任务的人员提供了清晰的参考。
英文摘要：
Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
