Many-to-many Cross-lingual Voice Conversion with a Jointly Trained Speaker Embedding Network
會議：2019 APSIPA 作者：ZHOUYI Li Haizhou 單位：新加坡國立 abstract average modeling approach用一個低維度的speaker embedding和vc網絡聯合訓練，可以達到many-to-many cross-lingual的效果。 base-model: vc+i-vector作爲speaker embedding表示。 introd
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相關文章
- 1. Voice Conversion Across Arbitrary Speakers based on a Single Target-Speaker Utterance
- 2. SINGING VOICE CONVERSION WITH NON-PARALLEL DATA
- 3. Neural Voice Cloning with a Few Samples
- 4. ONE-SHOT VOICE CONVERSION USING STAR-GAN
- 5. 行人屬性「Person Attribute Recognition with a Jointly-trained Holistic CNN Model」
- 6. X-VECTOR:ROBUST DNN EMBEDDING FOR SPEAKER RECOGNITION
- 7. 2019trans--Sequence-to-Sequence Acoustic Modeling for Voice Conversion
- 8. 2019icas--Cross-lingual Voice Conversion with Bilingual Phonetic PosteriorGram and Average Modeling
- 9. Odessey2018:Average Modeling Approach to Voice Conversion with Non-Parallel Data
- 10. 2018ins--Wavelet Analysis of Speaker Dependent and Independent Prosody for Voice Conversion