AffectSpeech: 一个带有精细文本描述的大型情感语音数据集，用于语音情感描述和合成
AffectSpeech: A Large-Scale Emotional Speech Dataset with Fine-Grained Textual Descriptions for Speech Emotion Captioning and Synthesis
AI总结：
本文提出AffectSpeech数据集，通过精细文本描述提升语音情感建模的表达能力，采用人机协作标注方法，实验表明其在情感描述和合成任务中表现优异。
摘要：
情绪在口语交流中至关重要，但现有语音情绪建模框架多依赖预定义类别或低维连续属性，表达能力有限。最近的语音情绪描述和合成进展表明，文本描述是更灵活和可解释的替代方案。然而，这一方向的进展受限于缺乏与可靠细粒度自然语言注释对齐的情感语音数据集。为此，我们引入AffectSpeech，一个包含人类录制语音的大型语料库，配有结构化描述以支持细粒度情绪分析和生成。每段语音在六个互补维度上进行特征化，包括情感极性、开放词汇情绪描述、强度级别、声学属性、显著片段和语义内容，从而实现多粒度的语音表达建模。为平衡注释质量和可扩展性，我们采用人机协作注释流程，结合算法预标注、多LLM描述生成和人类在环验证。此外，这些注释被改写成多种描述风格以增强语言多样性并减少下游建模中的风格偏差。在语音情绪描述和合成任务上的实验结果表明，训练于AffectSpeech的模型在多个评估设置中均表现出优越的性能。
英文摘要：
Emotion is essential in spoken communication, yet most existing frameworks in speech emotion modeling rely on predefined categories or low-dimensional continuous attributes, which offer limited expressive capacity. Recent advances in speech emotion captioning and synthesis have shown that textual descriptions provide a more flexible and interpretable alternative for representing affective characteristics in speech. However, progress in this direction is hindered by the lack of an emotional speech dataset aligned with reliable and fine-grained natural language annotations. To tackle this, we introduce AffectSpeech, a large-scale corpus of human-recorded speech enriched with structured descriptions for fine-grained emotion analysis and generation. Each utterance is characterized across six complementary dimensions, including sentiment polarity, open-vocabulary emotion captions, intensity level, prosodic attributes, prominent segments, and semantic content, enabling multi-granular modeling of vocal expression. To balance annotation quality and scalability, we adopt a human-LLM collaborative annotation pipeline that integrates algorithmic pre-labeling, multi-LLM description generation, and human-in-the-loop verification. Furthermore, these annotations are reformulated into diverse descriptive styles to enhance linguistic diversity and reduce stylistic bias in downstream modeling. Experimental results on speech emotion captioning and synthesis demonstrate that models trained on AffectSpeech consistently achieve superior performance across multiple evaluation settings.
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。
