Voice Conversion
151 papers with code • 2 benchmarks • 5 datasets
Voice Conversion is a technology that modifies the speech of a source speaker and makes their speech sound like that of another target speaker without changing the linguistic information.
Benchmarks
These leaderboards are used to track progress in Voice Conversion
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Libraries
Use these libraries to find Voice Conversion models and implementations
LibriSpeech
ESD
VESUS
Latest papers
FlashSpeech: Efficient Zero-Shot Speech Synthesis
The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation.
High-Fidelity Neural Phonetic Posteriorgrams
A phonetic posteriorgram (PPG) is a time-varying categorical distribution over acoustic units of speech (e. g., phonemes).
SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation
It comprises an autoregressive model based on LLM for semantic information modeling and a non-autoregressive model employing flow matching for perceptual information modeling.
DurFlex-EVC: Duration-Flexible Emotional Voice Conversion with Parallel Generation
Emotional voice conversion (EVC) seeks to modify the emotional tone of a speaker's voice while preserving the original linguistic content and the speaker's unique vocal characteristics.
AutoVisual Fusion Suite: A Comprehensive Evaluation of Image Segmentation and Voice Conversion Tools on HuggingFace Platform
This study presents a comprehensive evaluation of tools available on the HuggingFace platform for two pivotal applications in artificial intelligence: image segmentation and voice conversion.
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
cecile-hi/regularized-adaptive-weight-modification
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The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms.
HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis
Furthermore, we significantly improve the naturalness and speaker similarity of synthetic speech even in zero-shot speech synthesis scenarios.
Improving fairness for spoken language understanding in atypical speech with Text-to-Speech
Spoken language understanding (SLU) systems often exhibit suboptimal performance in processing atypical speech, typically caused by neurological conditions and motor impairments.
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
andersxa/cslp-ae
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NeurIPS 2023
While the present work only considers conversion of EEG, the proposed CSLP-AE provides a general framework for signal conversion and extraction of content (task activation) and style (subject variability) components of general interest for the modeling and analysis of biological signals.
Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-shot Speaker Adaptation
Finally, by using the masked prior in diffusion models, our model can improve the speaker adaptation quality.
