摘要：说话风格描述（Speaking-style captioning，简称 SSC）旨在生成关于语音表达方式的自然语言描述，捕捉音色、韵律和表现力等副语言属性。包括对话式 AI Agent、可控语音生成和大规模音频标注在内的众多下游应用，都需要针对特定维度的风格描述来刻画语音的特定方面（例如说话人特征、情绪或表达风格），而不是单一笼统的描述。然而，现有工作缺乏针对可控说话风格描述的统一形式化定义。为了弥补这一空白，我们将 SSC 形式化为一个指令遵循的音频语言任务，由明确的指令来指定需要描述的说话风格维度。我们推出了 StyleInstructCaps，这是首个用于音频语言模型中可控说话风格描述的标准化基准。StyleInstructCaps 提供了数据集和评估框架，用于衡量元数据真实可靠性（groundedness）、幻觉、指令遵循能力、对未见指令与音频数据集的泛化能力，以及说话人风格一致性。在对 9 种最先进的音频语言模型进行测试后，我们发现：较强的指令遵循能力并不一定意味着具备可靠的说话风格理解能力；而针对 SSC 开展具有指令多样性的训练，可以大幅提升模型的真实可靠性、可控性以及说话人一致性。

Abstract:Speaking-style captioning (SSC) aims to generate natural language descriptions of how speech is delivered, capturing paralinguistic attributes such as vocal timbre, prosody, and expressivity. Many downstream applications, including conversational AI agents, controllable speech generation, and large-scale audio annotation, require dimension-specific style captions that describe targeted aspects of speech (e.g., speaker traits, emotion, or delivery style), rather than a single undifferentiated description. However, existing work lacks a unified formulation for controllable speaking-style captioning. We address this gap by formulating SSC as an instruction-following audio-language task, where explicit instructions specify the speaking-style dimensions to be described. We introduce StyleInstructCaps, the first standardized benchmark for controllable speaking-style captioning in audio-language models. StyleInstructCaps provides a dataset and evaluation framework that measures metadata groundedness, hallucination, instruction-following ability, generalization to unseen instructions and audio datasets, and speaker-style consistency. Across nine state-of-the-art audio-language models, we find that strong instruction-following ability does not necessarily imply grounded speaking-style understanding, and that SSC-specific instruction-diverse training substantially improves groundedness, controllability, and speaker consistency.