While modern Text-to-Speech (TTS) systems achieve high fidelity for read-style speech, they struggle to generate Autonomous Sensory Meridian Response (ASMR), a specialized, low-intensity speech style essential for relaxation. The inherent challenges include ASMR's subtle, often unvoiced characteristics and the demand for zero-shot speaker adaptation. In this paper, we introduce DeepASMR, the first framework designed for zero-shot ASMR generation. We demonstrate that a single short snippet of a speaker's ordinary, read-style speech is sufficient to synthesize high-fidelity ASMR in their voice, eliminating the need for whispered training data from the target speaker. Methodologically, we first identify that discrete speech tokens provide a soft factorization of ASMR style from speaker timbre. Leveraging this insight, we propose a two-stage pipeline incorporating a Large Language Model (LLM) for content-style encoding and a flow-matching acoustic decoder for timbre reconstruction. Furthermore, we contribute DeepASMR-DB, a comprehensive 670-hour English-Chinese multi-speaker ASMR speech corpus, and introduce a novel evaluation protocol integrating objective metrics, human listening tests, LLM-based scoring and unvoiced speech analysis. Extensive experiments confirm that DeepASMR achieves state-of-the-art naturalness and style fidelity in ASMR generation for anyone of any voice, while maintaining competitive performance on normal speech synthesis.

翻译：尽管现代文本转语音（TTS）系统在朗读式语音上实现了高保真度，但它们难以生成自主感官经络反应（ASMR）这种对放松至关重要的、特殊的低强度语音风格。其固有挑战包括ASMR微妙且常为非浊音的特性，以及对零样本说话者自适应的需求。本文中，我们提出了DeepASMR，这是首个为零样本ASMR生成设计的框架。我们证明，仅需说话者一小段普通的朗读式语音片段，即可以其音色合成出高保真度的ASMR语音，从而无需目标说话者的耳语训练数据。在方法上，我们首先发现离散语音标记提供了ASMR风格与说话者音色的软因子分解。基于这一洞见，我们提出了一个两阶段流水线，结合了一个用于内容-风格编码的大语言模型（LLM）和一个用于音色重建的流匹配声学解码器。此外，我们贡献了DeepASMR-DB，一个包含670小时的英汉多说话者ASMR语音语料库，并引入了一种新颖的评估协议，该协议整合了客观指标、人类听感测试、基于LLM的评分以及非浊音语音分析。大量实验证实，DeepASMR在针对任意说话者任意音色的ASMR生成中，实现了最先进的自然度和风格保真度，同时在正常语音合成上保持了有竞争力的性能。