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Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme

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arxiv 2109.13821 v2 pith:MVQGO6KI submitted 2021-09-28 cs.SD cs.LGstat.ML

Vadim Popov , Ivan Vovk , Vladimir Gogoryan , Tasnima Sadekova , Mikhail Kudinov , Jiansheng Wei This is my paper · ORCID
classification cs.SDcs.LGstat.ML
keywords voiceconversiondiffusiongeneralone-shotqualitysynthesistarget
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Voice conversion is a common speech synthesis task which can be solved in different ways depending on a particular real-world scenario. The most challenging one often referred to as one-shot many-to-many voice conversion consists in copying the target voice from only one reference utterance in the most general case when both source and target speakers do not belong to the training dataset. We present a scalable high-quality solution based on diffusion probabilistic modeling and demonstrate its superior quality compared to state-of-the-art one-shot voice conversion approaches. Moreover, focusing on real-time applications, we investigate general principles which can make diffusion models faster while keeping synthesis quality at a high level. As a result, we develop a novel Stochastic Differential Equations solver suitable for various diffusion model types and generative tasks as shown through empirical studies and justify it by theoretical analysis.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks

    eess.AS 2026-08 conditional novelty 7.0 of 10

    A 260-hour emotional deepfake benchmark spanning 21 attack systems shows state-of-the-art speech deepfake detectors degrade badly on emotionally expressive and LALM-based spoofing.

  2. What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection

    cs.SD 2026-07 conditional novelty 6.0 of 10

    Underrepresented gender in training suffers higher deepfake-detection error; WavLM gaps stay large under balance, and all post-hoc calibrations leave the EER gap fixed at 1.317 pp.

  3. StarVC: A Unified Auto-Regressive Framework for Joint Text and Speech Generation in Voice Conversion

    cs.MM 2025-06 conditional novelty 6.0 of 10

    StarVC is an autoregressive voice conversion model that generates text tokens before acoustic tokens, improving linguistic fidelity while retaining speaker similarity.

  4. Approximate Borderline Sampling using Granular-Ball for Classification Tasks

    cs.CV 2025-06 conditional novelty 6.0 of 10

    GBABS samples only approximate borderline points detected via non-overlapping granular balls, reporting better classifier accuracy and noise robustness than GB-based and standard sampling baselines.

  5. EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion

    eess.AS 2024-12 conditional novelty 6.0 of 10

    EmoReg controls emotional intensity in diffusion-based voice conversion by scaling a PCA-projected direction vector in a fine-tuned self-supervised emotion embedding space.

  6. Zero-shot Voice Conversion with Diffusion Transformers

    cs.SD 2024-11 conditional novelty 6.0 of 10

    A diffusion transformer with a training-time timbre shifter improves zero-shot voice conversion similarity and intelligibility, and extends to singing conversion with F0 conditioning.

  7. MoLEx: Mixture of LoRA Experts in Speech Self-Supervised Models for Audio Deepfake Detection

    cs.SD 2025-09 conditional novelty 5.0 of 10

    MoLEx combines LoRA adapters with a top-K expert router inside a frozen WavLM model, achieving 5.56% EER on ASVSpoof 5 without augmentation.

  8. ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization

    cs.SD 2025-06 conditional novelty 5.0 of 10

    A rectified-flow voice conversion model with speaker feature fusion achieves zero-shot conversion in one sampling step with quality close to 30-step diffusion baselines.

  9. Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching

    eess.AS 2025-06 conditional novelty 5.0 of 10

    R-VC performs zero-shot voice conversion in two sampling steps while transferring the target speaker's rhythm, matching or exceeding prior systems in naturalness and intelligibility.

  10. Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning

    cs.SD 2025-01 reject novelty 5.0 of 10

    Stepback trains a voice converter with two decoders and a self-destructive loss to separate speaker identity from linguistic content, but the preprint contains no reported evaluation results.

  11. EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion

    cs.SD 2025-05 reject novelty 4.0 of 10

    EZ-VC combines discrete units from a multilingual self-supervised encoder (Xeus) with an F5-TTS flow-matching decoder to achieve zero-shot any-to-any voice conversion, without text labels or multiple disentangling encoders.

  12. DiffAttack: Diffusion-based Timbre-reserved Adversarial Attack in Speaker Identification

    cs.SD 2025-01 conditional novelty 4.0 of 10

    DiffAttack injects adversarial constraints into the reverse diffusion process of DiffVC, boosting targeted speaker-identification attack success from 28.4% to 65.8% on LibriTTS while retaining speech quality.

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