Search Results for author:
Found 4 papers, 3 papers with code
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning
In this paper, we aim to build TTS systems for such low-resource (target) languages where only very limited paired data are available.
One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization
Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-target scenario in which a single model is trained to convert the input voice to many different speakers.
Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences
In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data.
Sound Audio and Speech Processing
Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations
The decoder then takes the speaker-independent latent representation and the target speaker embedding as the input to generate the voice of the target speaker with the linguistic content of the source utterance.
