让新闻对话更有人情味，自动调节语气增强共情。
EmoNews: A Spoken Dialogue System for Expressive News Conversations
- 用大模型分析上下文情绪，驱动语音情感合成。
- 用户评估显示新系统情绪调节与参与度显著提升。
- 适合研究人机共情对话或语音交互的开发者。
我们开发了一个面向新闻对话的任务导向语音对话系统（SDS），通过基于上下文线索调节语音情感，实现更具同理心的对话体验。尽管情感文本转语音（TTS）技术已有进展，但任务导向的情感对话系统仍因对话系统与情感语音研究的割裂，以及缺乏社交目标的标准化评估指标而发展滞后。为此，我们构建了一种新闻对话情感系统，采用大语言模型（LLM）进行情绪识别，并利用PromptTTS生成符合语境的情感语音。同时提出主观评价量表，评估所提系统与基线系统的表情达能力。实验表明，我们的系统在情绪调节和用户参与度上均优于基线。结果表明语音情感对提升对话吸引力具有关键作用。所有源代码已开源：https://github.com/dhatchi711/espnet-emotional-news/tree/emo-sds/egs2/emo_news_sds/sds1。
原文摘要 · Abstract (English)
We develop a task-oriented spoken dialogue system (SDS) that regulates emotional speech based on contextual cues to enable more empathetic news conversations. Despite advancements in emotional text-to-speech (TTS) techniques, task-oriented emotional SDSs remain underexplored due to the compartmentalized nature of SDS and emotional TTS research, as well as the lack of standardized evaluation metrics for social goals. We address these challenges by developing an emotional SDS for news conversations that utilizes a large language model (LLM)-based sentiment analyzer to identify appropriate emotions and PromptTTS to synthesize context-appropriate emotional speech. We also propose subjective evaluation scale for emotional SDSs and judge the emotion regulation performance of the proposed and baseline systems. Experiments showed that our emotional SDS outperformed a baseline system in terms of the emotion regulation and engagement. These results suggest the critical role of speech emotion for more engaging conversations. All our source code is open-sourced at https://github.com/dhatchi711/espnet-emotional-news/tree/emo-sds/egs2/emo_news_sds/sds1
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。