通过等级语音模拟和声学扩散去除电影的配音
概括
HD-Dubber通过使用等级语音模拟 (HPM) 和声学扩散排斥 (ADD) 来改善电影配音,以实现更好的唇部同步和语音克隆. 这种基于扩散的新方法在生成的演讲中增强了发音和演讲者相似性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 语音合成 语音合成
背景情况:
- 电影配音 (视觉语音克隆,V2C) 合成语音匹配视频唇部运动和情绪.
- 现有的方法往往会产生语,原因是对音符的 misalignment.
- 生成同步和情感共的配音音频是一个重大挑战.
研究的目的:
- 提出一种新的基于扩散的架构,HD-Dubber,用于高质量的电影配音.
- 为了提高合成演讲的发音准确性和演讲者相似性.
- 为了增强生成的音频和视频内容之间的对齐.
主要方法:
- 层次语音模拟 (HPM) 用于将唇部运动,面部表情和全球情绪与语音表情调整.
- 声波扩散排泄 (ADD) 使用新型排泄器 (SRD,PUD) 的扩散框架用于mel频谱生成.
- 用对比式学习来调整音符级别的口唇同步持续时间.
主要成果:
- HD-Dubber在基准数据集上展示了最先进的性能.
- 拟议的HPM有效地将视觉信息与语音表达联系起来.
- 通过先进的消音技术,ADD增强了说话者的相似性和发音质量.
结论:
- 基于扩散的HD-Dubber模型显著推进了电影配音技术.
- 层次语音模拟和声学扩散排斥是提高配音质量的关键.
- 该方法为生成自然和同步配音音频提供了强大的解决方案.
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