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StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks

arXiv 1806.02169 ↗First posted 6 Jun 2018cs.SD cs.LG eess.AScited by 78

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Abstract (arXiv)

This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it (1) requires no parallel utterances, transcriptions, or time alignment procedures for speech generator training, (2) simultaneously learns many-to-many mappings across different attribute domains using a single generator network, (3) is able to generate converted speech signals quickly enough to allow real-time implementations and (4) requires only several minutes of training examples to generate reasonably realistic-sounding speech. Subjective evaluation experiments on a non-parallel many-to-many speaker identity conversion task revealed that the proposed method obtained higher sound quality and speaker similarity than a state-of-the-art method based on variational autoencoding GANs.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2018.

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Citing papers per year: 2021: 26; 2022: 16; 2023: 11; 2024: 7; 2025: 12; 2026: 6.

78 papers.

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  1. 2023Voicebox: Text-Guided Multilingual Universal Speech Generation at Scalecited by 373
  2. 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 113
  3. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  4. 2022ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakerscited by 73
  5. 2021VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversioncited by 63
  6. 2022An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Eracited by 32
  7. 2022Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Datacited by 30
  8. 2022Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversioncited by 28
  9. 2021Towards Relatable Explainable AI with the Perceptual Processcited by 27
  10. 2021MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Framescited by 18
  11. 2022Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognitioncited by 16
  12. 2022Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Unitscited by 15
38 more
  1. 2021"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real Worldcited by 12
  2. 2021Style Equalization: Unsupervised Learning of Controllable Generative Sequence Modelscited by 11
  3. 2022Towards Improved Zero-shot Voice Conversion with Conditional DSVAEcited by 10
  4. 2021NVC-Net: End-to-End Adversarial Voice Conversioncited by 9
  5. 2022Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detectioncited by 9
  6. 2022SpecRNet: Towards Faster and More Accessible Audio DeepFake Detectioncited by 9
  7. 2024Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfercited by 9
  8. 2021VocBench: A Neural Vocoder Benchmark for Speech Synthesiscited by 8
  9. 2023Face-Driven Zero-Shot Voice Conversion with Memory-based Face-Voice Alignmentcited by 8
  10. 2024DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text Alignmentcited by 8
  11. 2021An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentationcited by 7
  12. 2021Training Robust Zero-Shot Voice Conversion Models with Self-supervised Featurescited by 7
  13. 2022Trusted Media Challenge Dataset and User Studycited by 7
  14. 2024Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representationscited by 7
  15. 2024FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillationcited by 7
  16. 2021Review of end-to-end speech synthesis technology based on deep learningcited by 6
  17. 2021Global Rhythm Style Transfer Without Text Transcriptionscited by 6
  18. 2021Zero-shot Voice Conversion via Self-supervised Prosody Representation Learningcited by 6
  19. 2021How Deep Are the Fakes? Focusing on Audio Deepfake: A Surveycited by 6
  20. 2022Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graphcited by 6
  21. 2023Towards General-Purpose Text-Instruction-Guided Voice Conversioncited by 6
  22. 2023CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversioncited by 6
  23. 2021StarGAN-based Emotional Voice Conversion for Japanese Phrasescited by 4
  24. 2021NoiseVC: Towards High Quality Zero-Shot Voice Conversioncited by 4
  25. 2021StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contextscited by 4
  26. 2021Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environmentscited by 4
  27. 2021Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networkscited by 4
  28. 2022TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Trainingcited by 4
  29. 2023Catch You and I Can: Revealing Source Voiceprint Against Voice Conversioncited by 4
  30. 2025ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimizationcited by 4
  31. 2025RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Codingcited by 4
  32. 2021An Adaptive Learning based Generative Adversarial Network for One-To-One Voice Conversioncited by 3
  33. 2021Toward Degradation-Robust Voice Conversioncited by 3
  34. 2022Speak Like a Dog: Human to Non-human creature Voice Conversioncited by 3
  35. 2021Conditional Deep Hierarchical Variational Autoencoder for Voice Conversioncited by 2
  36. 2022GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversioncited by 2
  37. 2023AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignmentcited by 2
  38. 2024Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraintscited by 2

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