研究基于混合调整机制的语音合成.
Yan Deng1, Ning Wu2, Chengjun Qiu3,4
1School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
混合TTS是一种新的非自回归语音合成模型,使用混合对齐机制来增强文本对音频的对齐. 这种深度学习方法实现了高质量的语音合成,并改进了节奏和语义信息提取.
科学领域:
- 机器学习 机器学习
- 语音合成 语音合成
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 基于深度学习的语音合成模型近年来获得了显著的引力.
- 优化文本和音频表示之间的对齐对于具有自然声音的语音合成至关重要.
研究的目的:
- 提出混合TTS,一种非自律回归的语音合成模型.
- 通过混合调整机制,增强文本序列和mel光谱图之间的对齐.
主要方法:
- 采用具有软音符级和硬词级对齐的语言编码器,用于语义信息提取.
- 集成音调和能量预测器,提供准确的节奏信息.
- 使用五层1D卷积网络后网用于mel-spectrogram重新配置.
- 利用HiFi-GAN声编码器进行最终的音频生成.
主要成果:
- 混合TTS显示了文本和Mel光谱图之间的更好的对齐.
- 该模型实现了高质量的音频输出.
- 废弃性研究证实了拟议的混合TTS架构的有效性.
结论:
- 混合TTS为非自律回归语音合成提供了一种有效的方法.
- 混合调整机制显著有助于改进文本音频同步和音频质量.
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