唱歌到语音转换与生成流的流动
Jiawen Huang1, Emmanouil Benetos1
1Centre for Digital Music, Queen Mary University of London, London, UK.
概括
这项研究介绍了首个深度学习系统,用于歌唱到语音 (S2S) 转换,将歌唱转换为自然发声的语音,同时保留语音细节. 该S2S模型显示了改善低资源歌词转录的承诺.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 唱歌到语音 (S2S) 转换是一个全新的跨领域任务.
- 现有的方法缺乏自然性和语音准确性.
- 深度学习方法为S2S的发展提供了潜力.
研究的目的:
- 推出第一个基于深度学习的S2S系统.
- 将歌曲转化为语音,保存语音内容.
- 在转换的语音中增强自然性和语音相似性.
主要方法:
- 开发了一种受 Glow-TTS.TS 启发的生成流模型.
- 适应S2S的单调对齐搜索 (MAS).
- 使用一个持续时间预测器来处理模式差异.
主要成果:
- 拟议的深度学习模型在自然性方面表现优于信号处理基线.
- 它在语音相似性方面超过了转录和合成方法.
- 在低资源歌词转录中,S2S转换被证明是有效的.
结论:
- 新型深度学习S2S系统实现了高度的自然性和语音准确性.
- 这项技术为解决语音转换挑战提供了可行的解决方案.
- S2S转换可以作为一种有价值的数据增强技术.
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