eCat：一种用于多说话人TTS与多对多细粒度韵律迁移的端到端模型
eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer
AI总结：
eCat是一种端到端多说话人TTS模型，通过两阶段学习词级韵律表示，实现细粒度韵律迁移，显著提升自然度并减少46.7%的差距。
摘要：
我们提出eCat，一种新颖的端到端多说话人模型，能够实现：a）生成具有表现力和上下文适当韵律的长上下文语音，以及b）在任意一对已见说话人之间进行细粒度韵律迁移。eCat采用两阶段训练方法进行训练。在第一阶段，模型以端到端方式从语音中学习说话人无关的词级韵律表示。在第二阶段，我们利用文本中可用的上下文信息来学习预测韵律表示。我们将eCat与CopyCat2进行比较，后者是一种能够同时进行细粒度韵律迁移（FPT）和多说话人TTS的模型。我们表明，eCat在2种语言、3个地区和7个说话人上，平均将CopyCat2与人类录音之间的自然度差距统计显著地减少了46.7%，并且在FPT中具有更好的目标说话人相似度。我们还将eCat与VITS进行比较，并显示出统计显著的偏好。
英文摘要：
We present eCat, a novel end-to-end multispeaker model capable of: a) generating long-context speech with expressive and contextually appropriate prosody, and b) performing fine-grained prosody transfer between any pair of seen speakers. eCat is trained using a two-stage training approach. In Stage I, the model learns speaker-independent word-level prosody representations in an end-to-end fashion from speech. In Stage II, we learn to predict the prosody representations using the contextual information available in text. We compare eCat to CopyCat2, a model capable of both fine-grained prosody transfer (FPT) and multi-speaker TTS. We show that eCat statistically significantly reduces the gap in naturalness between CopyCat2 and human recordings by an average of 46.7% across 2 languages, 3 locales, and 7 speakers, along with better target-speaker similarity in FPT. We also compare eCat to VITS, and show a statistically significant preference.
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。
