2018ins--Wavelet Analysis of Speaker Dependent and Independent Prosody for Voice Conversion
单位：新加坡国立 作者：Haizhou Li abstract 现在的vc基本都是基于谱做的变换，但是和说话者相关的韵律特征，比如基频、能量包络，我们认为如果能量更好的理解基频，就能更好的实现更好的vc效果。说话者依赖的特征是说话者的特性，说话者独立的特征是语言表达的特性，在vc任务中，前者是需要转换的，后者是需要保留的。我们提出用wavelet在不同的时间尺度分析这两个特征。 （谱参数与音色有
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相关文章
- 1. Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody
- 2. Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification
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- 6. Many-to-many Cross-lingual Voice Conversion with a Jointly Trained Speaker Embedding Network
- 7. 2016 ICME:Phonetic posteriorgrams for many-to-one voice conversion without parallel data training
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- 9. x-vector：Deep Neural Network Embeddings for Text-Independent Speaker Verification
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