MOSS-TTSD：文本到 spoken 对话生成
MOSS-TTSD: Text to Spoken Dialogue Generation
AI总结：
MOSS-TTSD 提出了一种多语言多轮对话生成模型，通过长上下文建模实现稳定、多角色的语音对话生成，支持零样本语音克隆和多种长形式场景。
摘要：
MOSS-TTSD 提出了一种多语言多轮对话生成模型，通过长上下文建模实现稳定、多角色的语音对话生成，支持零样本语音克隆和多种长形式场景。
英文摘要：
Spoken dialogue generation is crucial for applications like podcasts, dynamic commentary, and entertainment content, but poses significant challenges compared to single-utterance text-to-speech (TTS). Key requirements include accurate turn-taking, cross-turn acoustic consistency, and long-form stability, which current models often fail to address due to a lack of dialogue context modeling. To bridge this gap, we present MOSS-TTSD, a spoken dialogue synthesis model designed for expressive, multi-party conversational speech across multiple languages. With enhanced long-context modeling, MOSS-TTSD generates long-form spoken conversations from dialogue scripts with explicit speaker tags, supporting up to 60 minutes of single-pass synthesis, multi-party dialogue with up to 5 speakers, and zero-shot voice cloning from a short reference audio clip. The model supports various mainstream languages, including English and Chinese, and is adapted to several long-form scenarios. Additionally, to address limitations of existing evaluation methods, we propose TTSD-eval, an objective evaluation framework based on forced alignment that measures speaker attribution accuracy and speaker similarity without relying on speaker diarization tools. Both objective and subjective evaluation results show that MOSS-TTSD surpasses strong open-source and proprietary baselines in dialogue synthesis.
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。
