Spoken DialogSum：用于语音对话摘要的富有情感的对话数据集
Spoken DialogSum: An Emotion-Rich Conversational Dataset for Spoken Dialogue Summarization
AI总结：
Spoken DialogSum是一个用于语音对话摘要的富有情感的对话数据集，通过结合语音、摘要和非语言线索，提升情感摘要的生成效果。
摘要：
最近的音频语言模型能够跟踪长时间的对话。然而，关于具有情感意识或语音对话摘要的研究受到缺乏将语音、摘要和非语言线索联系起来的数据的限制。我们介绍了Spoken DialogSum，这是第一个将原始对话音频与事实性摘要、情感丰富的摘要以及说话者年龄、性别和情感的语句级标签对齐的语料库。该数据集分为两个阶段构建：首先，一个LLM重写DialogSum脚本，使用Switchboard风格的填充词和反馈信号，然后对每个陈述进行情感、音调和说话速度的标注。其次，一个表现力强的TTS引擎从标注的脚本中合成语音，并与非语言标签对齐。Spoken DialogSum包含13,460个情感多样的对话，每个对话都配有事实性和情感聚焦的摘要。我们发布了在线演示，网址为https://fatfat-emosum.github.io/EmoDialog-Sum-Audio-Samples/，并计划在未来不久发布完整数据集。基线结果显示，音频LLM在情感摘要的ROUGE-L指标上比级联的ASR-LLM系统提高了28%，证实了端到端语音建模的价值。
英文摘要：
Recent audio language models can follow long conversations. However, research on emotion-aware or spoken dialogue summarization is constrained by the lack of data that links speech, summaries, and paralinguistic cues. We introduce Spoken DialogSum, the first corpus aligning raw conversational audio with factual summaries, emotion-rich summaries, and utterance-level labels for speaker age, gender, and emotion. The dataset is built in two stages: first, an LLM rewrites DialogSum scripts with Switchboard-style fillers and back-channels, then tags each utterance with emotion, pitch, and speaking rate. Second, an expressive TTS engine synthesizes speech from the tagged scripts, aligned with paralinguistic labels. Spoken DialogSum comprises 13,460 emotion-diverse dialogues, each paired with both a factual and an emotion-focused summary. We release an online demo at https://fatfat-emosum.github.io/EmoDialog-Sum-Audio-Samples/, with plans to release the full dataset in the near future. Baselines show that an Audio-LLM raises emotional-summary ROUGE-L by 28% relative to a cascaded ASR-LLM system, confirming the value of end-to-end speech modeling.
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。
