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Voice Cloning

Voice cloning is a highly desired feature for personalized speech interfaces. Neural voice cloning system learns to synthesize a person’s voice from only a few audio samples.

Papers

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Showing 76–100 of 112 papers

TitleStatusHype
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis—0
Taiwanese-Accented Mandarin and English Multi-Speaker Talking-Face Synthesis System—0
Low-Resource Multilingual and Zero-Shot Multispeaker TTSCode0
Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech SynthesisCode0
Mix and Match: An Empirical Study on Training Corpus Composition for Polyglot Text-To-Speech (TTS)—0
Unsupervised TTS Acoustic Modeling for TTS with Conditional Disentangled Sequential VAE—0
Dictionary Attacks on Speaker VerificationCode0
Self-supervised learning for robust voice cloning—0
Improve few-shot voice cloning using multi-modal learning—0
Zero-Shot Long-Form Voice Cloning with Dynamic Convolution Attention—0
V2C: Visual Voice Cloning—0
Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning—0
SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and MachinesCode0
Revisiting IPA-based Cross-lingual Text-to-speech—0
Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data—0
Exploring Timbre Disentanglement in Non-Autoregressive Cross-Lingual Text-to-Speech—0
Adapting TTS models For New Speakers using Transfer Learning—0
Discovery of Single Independent Latent VariableCode0
Translatotron 2: High-quality direct speech-to-speech translation with voice preservation—0
AI based Presentation Creator With Customized Audio Content Delivery—0
Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance—0
The AS-NU System for the M2VoC Challenge—0
The Multi-speaker Multi-style Voice Cloning Challenge 2021—0
CUHK-EE Voice Cloning System for ICASSP 2021 M2VoC Challenge—0
Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-SpeechCode0
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