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2018ins--Wavelet Analysis of Speaker Dependent and Independent Prosody for Voice Conversion
時間 2021-01-11
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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.
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4.
2019icas--Cross-lingual Voice Conversion with Bilingual Phonetic PosteriorGram and Average Modeling
5.
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6.
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7.
2016 ICME:Phonetic posteriorgrams for many-to-one voice conversion without parallel data training
8.
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9.
x-vector:Deep Neural Network Embeddings for Text-Independent Speaker Verification
10.
ONE-SHOT VOICE CONVERSION USING STAR-GAN
>>更多相關文章<<