Voice Conversion
149 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
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.
Low-latency Real-time Voice Conversion on CPU
koeai/llvc
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To our knowledge LLVC achieves both the lowest resource usage as well as the lowest latency of any open-source voice conversion model.
