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eess.AS2022★ 1 cited
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
Ziqian Ning, Qicong Xie, Pengcheng Zhu +5
Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To addr…
cs.SD2022
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
Liumeng Xue, Frank K. Soong, Shaofei Zhang +1
Recent advancements in neural end-to-end TTS models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to re…
cs.SD2022
Learning Noise-independent Speech Representation for High-quality Voice Conversion for Noisy Target Speakers
Liumeng Xue, Shan Yang, Na Hu +2
Building a voice conversion system for noisy target speakers, such as users providing noisy samples or Internet found data, is a challenging task since the use of contaminated spee…