VECL-TTS：语音身份与情感风格可控的跨语言文本到语音合成
VECL-TTS: Voice identity and Emotional style controllable Cross-Lingual Text-to-Speech
AI总结：
本文提出VECL-TTS端到端跨语言语音合成系统，同时控制语音身份与情感风格，并通过内容与风格一致性损失提升合成质量，在英语及三种印度语言上较SOTA方法平均相对提升8.83%。
摘要：
尽管文本到语音（TTS）系统取得了显著进展，但其在自动配音中的充分利用仍然有限。该任务需要从源语言的参考语音中提取语音身份和情感风格，随后利用跨语言TTS技术将其迁移到目标语言。以往的方法主要集中于在跨语言TTS框架内控制语音身份，而将情感与语音身份同时结合的工作较为有限。为此，我们引入了一个端到端的语音身份与情感风格可控跨语言（VECL）TTS系统，该系统使用多语言说话人和情感嵌入网络。此外，我们引入了内容与风格一致性损失，以进一步提升合成语音的质量。所提出的系统在包含英语和三种印度语言（印地语、泰卢固语和马拉地语）的数据库上，与最先进（SOTA）方法相比，取得了8.83%的平均相对改进。
英文摘要：
Despite the significant advancements in Text-to-Speech (TTS) systems, their full utilization in automatic dubbing remains limited. This task necessitates the extraction of voice identity and emotional style from a reference speech in a source language and subsequently transferring them to a target language using cross-lingual TTS techniques. While previous approaches have mainly concentrated on controlling voice identity within the cross-lingual TTS framework, there has been limited work on incorporating emotion and voice identity together. To this end, we introduce an end-to-end Voice Identity and Emotional Style Controllable Cross-Lingual (VECL) TTS system using multilingual speakers and an emotion embedding network. Moreover, we introduce content and style consistency losses to enhance the quality of synthesized speech further. The proposed system achieved an average relative improvement of 8.83\% compared to the state-of-the-art (SOTA) methods on a database comprising English and three Indian languages (Hindi, Telugu, and Marathi).
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。
