EmoNews：一个用于富有表现力的新闻对话的口语对话系统
EmoNews: A Spoken Dialogue System for Expressive News Conversations
AI总结：
本研究开发了用于新闻对话的情感口语对话系统EmoNews，结合LLM情感分析器与PromptTTS实现上下文适配的情感语音，提出对应评估量表，实验证明其在情感调节与对话参与度上优于基线系统，且代码已开源。
摘要：
我们开发了一个任务导向型口语对话系统（SDS），该系统可根据上下文线索调节情感语音，以实现更具共情力的新闻对话。尽管情感文本转语音（TTS）技术取得了进展，但由于口语对话系统与情感TTS研究的相互割裂，以及缺乏针对社交目标的标准化评估指标，任务导向型情感口语对话系统仍未得到充分探索。我们通过开发一款用于新闻对话的情感口语对话系统来应对这些挑战，该系统利用基于大语言模型（LLM）的情感分析器识别合适的情感，并使用PromptTTS合成符合上下文的情感语音。我们还提出了针对情感口语对话系统的主观评估量表，并对所提出的系统和基线系统的情感调节性能进行了评判。实验表明，我们的情感口语对话系统在情感调节和参与度方面均优于基线系统。这些结果表明，语音情感对于提升对话参与度具有关键作用。我们的所有源代码均已在https://github.com/dhatchi711/espnet-emotional-news/tree/emo-sds/egs2/emo_news_sds/sds1 开源。
英文摘要：
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
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。
