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高保真零射击扬声器在文本中适应语音合成,具有无噪声扩散的GAN
Xiangchun Liu1, Xuan Ma1, Wei Song2,3,4,5
1School of Information Engineering, Minzu University of China, Beijing, 100081, China.
Scientific reports
|February 20, 2025
概括
这项研究介绍了DiffGAN-ZSTTS,一个高效的零射击扬声器适应性文本到语音 (TTS) 模型. 它显著提高了合成语音质量和语音相似性,用于使用最小语音样本的看不见的声音.
科学领域:
- 语音合成和语言处理
- 人工智能和机器学习
- 数字信号处理是数字信号处理.
背景情况:
- 零射击扬声器适应旨在为看不见的扬声器从有限的语音样本中克隆声音.
- 现有的系统显示,在看不见的和见到的扬声器之间,语音质量和扬声器相似性存在差异.
- 改进未见扬声器的零射击TTS仍然是一个重大挑战.
研究的目的:
- 为了引入一个高效的零射击扬声器适应型TTS模型,DiffGAN-ZSTTS.
- 为了提高合成语音质量和语音相似性,用于看不见的扬声器.
- 提高TTS模型在零射击设置中的概括能力.
主要方法:
- 开发了基于FastSpeech2框架的DiffGAN-ZSTTS,使用基于扩散的解码器.
- 引入了SE-Res2FFT模块,用于编码器中平衡的本地和全球特征提取.
- 整合了MHSE模块,以增强扬声器参考音频特征表示.
主要成果:
- DiffGAN-ZSTTS在合成语音质量和发言者相似性方面表现出显著的改善.
- 该模型成功地使用最小的数据对未见的扬声器进行了零射击语音合成.
- 在各种数据集 (AISHELL3,LibriTTS,Baker,VCTK) 上以中英两种语言验证了性能,性能优于最先进的模型.
结论:
- DiffGAN-ZSTTS有效地解决了目前为TTS的零射击扬声器适应的局限性.
- 拟议的模型在扬声器相似性和未见扬声器的音频质量方面都实现了卓越的性能.
- 这项工作推进了高保真性和高效率的零射击语音克隆的能力.
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