2018ins--Wavelet Analysis of Speaker Dependent and Independent Prosody for Voice Conversion
單位：新加坡國立 作者：Haizhou Li abstract 現在的vc基本都是基於譜做的變換，但是和說話者相關的韻律特徵，比如基頻、能量包絡，我們認爲如果能量更好的理解基頻，就能更好的實現更好的vc效果。說話者依賴的特徵是說話者的特性，說話者獨立的特徵是語言表達的特性，在vc任務中，前者是需要轉換的，後者是需要保留的。我們提出用wavelet在不同的時間尺度分析這兩個特徵。 （譜參數與音色有
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相關文章
- 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
- 3. 2019trans--Sequence-to-Sequence Acoustic Modeling for Voice Conversion
- 4. 2019icas--Cross-lingual Voice Conversion with Bilingual Phonetic PosteriorGram and Average Modeling
- 5. Whether To Pretrain DNN or Not?: An Empirical Analysis for Voice Conversion
- 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
- 8. Voice Conversion Across Arbitrary Speakers based on a Single Target-Speaker Utterance
- 9. x-vector：Deep Neural Network Embeddings for Text-Independent Speaker Verification
- 10. ONE-SHOT VOICE CONVERSION USING STAR-GAN