自然语言:端到端的文本到语音合成与人类水平的质量
IEEE transactions on pattern analysis and machine intelligence
|January 19, 2024
概括
研究人员开发了NaturalSpeech,这是一个文本转化为语音 (TTS) 系统,实现了人类水平的质量. 该系统通过统计学意义来定义和判断质量,标志着TTS技术的首次出现.
科学领域:
- 人工智能的人工智能
- 语音合成 语音合成
- 机器学习 机器学习
背景情况:
- 文本转化为语音 (TTS) 技术的近期进展引发了关于实现人类水平质量的问题.
- 定义和客观地衡量TTS质量仍然是一个挑战.
研究的目的:
- 定义和建立文本到语音系统中人类水平质量的标准.
- 开发一个TTS系统,NaturalSpeech,能够达到人类水平的语音质量.
- 通过严格的统计方法,对人类记录来验证系统的性能.
主要方法:
- 利用变化自动编码器 (VAE) 来进行端到端的文本到波形生成.
- 实施关键模块:语音预训练,可微分持续时间建模,双向前后建模和VAE记忆机制.
- 建立基于统计学上显著的主观措施的质量判断准则.
主要成果:
- 在LJSpeech数据集上,NaturalSpeech系统获得了0.01的相对平均意见得分 (CMOS).
- 统计分析 (Wilcoxon签名排名测试) 显示NaturalSpeech和人类记录之间没有显著差异 (p>0.05).
- 这是TTS系统首次在统计学上表现出与人类言语无法区分的质量.
结论:
- 自然语言成功地实现了人类水平的文本到语音质量.
- 该研究为定义和评估TTS质量提供了一个强大的框架.
- 开发的方法为更自然和更类似于人类的合成语音生成铺平了道路.
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