Abstract
Recent advances in singing voice synthesis (SVS) have attracted substantial attention from both academia and industry. With the advent of large language models and novel generative paradigms, producing controllable, high-fidelity singing voices has become an attainable goal. Yet the field still lacks a comprehensive survey that systematically analyzes deep-learning-based singing voice synthesis systems and their enabling technologies. To address the aforementioned issue, this survey first categorizes existing systems by task type and then organizes current architectures into two major paradigms: cascaded and end-to-end approaches. Moreover, we provide an in-depth analysis of core technologies, covering singing modeling and control techniques. Finally, we review relevant datasets, annotation tools, and evaluation benchmarks that support training and assessment. In appendix, we introduce training strategies and further discussion of SVS. This survey provides an up-to-date review of the literature on SVS models, which would be a useful reference for both researchers and engineers. Related materials are available at this https URL.
Abstract (translated)
近期在歌声合成（SVS）领域的进展吸引了学术界和工业界的广泛关注。随着大型语言模型和新型生成范式的出现，生产可控的、高保真的歌声已经成为一个可实现的目标。然而，该领域仍然缺乏对基于深度学习的歌声合成系统及其关键技术进行全面分析的综述文献。为了应对上述问题，本综述首先根据任务类型对现有系统进行分类，并将当前架构组织为两大范式：级联和端到端方法。此外，我们还深入分析了核心技术，包括歌唱建模和控制技术。最后，我们回顾了支持训练和评估的相关数据集、标注工具及评价基准。在附录中，我们介绍了培训策略以及关于SVS的进一步讨论。本综述提供了对歌声合成模型文献的最新回顾，将为研究人员和工程师提供有用的参考资源。相关材料可在[此处](https://example.com)访问。
URL
https://arxiv.org/abs/2601.13910
