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The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

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arxiv 1804.04262 v1 pith:2INIAEHQ submitted 2018-04-12 eess.AS cs.CLcs.SDstat.ML

Jaime Lorenzo-Trueba , Junichi Yamagishi , Tomoki Toda , Daisuke Saito , Fernando Villavicencio , Tomi Kinnunen , Zhenhua Ling This is my paper · ORCID
classification eess.AScs.CLcs.SDstat.ML
keywords challengeconversionspeakervoiceidentityparallelspokestate-of-the-art
verification ladder T0 review T1 audit T2 compute T3 formal

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We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e. transform the vocal identity) of a source speaker to a target speaker while maintaining linguistic information. As an update to the previous challenge, we considered both parallel and non-parallel data to form the Hub and Spoke tasks, respectively. A total of 23 teams from around the world submitted their systems, 11 of them additionally participated in the optional Spoke task. A large-scale crowdsourced perceptual evaluation was then carried out to rate the submitted converted speech in terms of naturalness and similarity to the target speaker identity. In this paper, we present a brief summary of the state-of-the-art techniques for VC, followed by a detailed explanation of the challenge tasks and the results that were obtained.

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Cited by 7 Pith papers

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

  1. PseudoVC: Improving One-shot Voice Conversion with Pseudo Paired Data

    eess.AS 2025-06 conditional novelty 6.0 of 10

    A two-part training strategy that uses a pretrained voice conversion model to generate pseudo paired data and same-speaker sampling to reduce train-inference mismatch, improving one-shot voice conversion over FreeVC a...

  2. SongEval: A Benchmark Dataset for Song Aesthetics Evaluation

    eess.AS 2025-05 conditional novelty 6.0 of 10

    SongEval is a 140-hour benchmark of full-length generated songs rated by expert musicians on five aesthetic dimensions, and trained predictors outperform objective metrics at matching human ratings.

  3. Towards Multimodal Empathetic Response Generation: A Rich Text-Speech-Vision Avatar-based Benchmark

    cs.MM 2025-02 conditional novelty 6.0 of 10

    AvaMERG is a new text-speech-vision avatar benchmark for empathetic response generation, and the Empatheia system is claimed to outperform baselines on both textual and multimodal empathy tasks.

  4. Non-Parallel Voice Conversion with Cyclic Variational Autoencoder

    eess.AS 2019-07 unverdicted novelty 6.0 of 10

    CycleVAE optimizes non-parallel voice conversion indirectly via cyclic reconstructed spectra, yielding higher spectral accuracy, latent feature correlation, and improved converted speech quality.

  5. Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion

    cs.SD 2025-04 reject novelty 5.0 of 10

    A single-generator, three-discriminator GAN with a collective weighting rule and an optimal transport loss is claimed to improve non-parallel voice conversion over MaskCycleGAN-VC and MelGAN-VC.

  6. SALF-MOS: Speaker Agnostic Latent Features Downsampled for MOS Prediction

    cs.SD 2025-06 reject novelty 4.0 of 10

    SALF-MOS, a compact U-Net-style model using frozen wav2vec features, claims state-of-the-art MOS prediction on four benchmarks with only 1,574 parameters.

  7. FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning

    eess.AS 2025-04 conditional novelty 4.0 of 10

    FADEL replaces softmax with a Dirichlet-based evidential output in anti-spoofing models, improving EER on ASVspoof2019 LA and ASVspoof2021 LA over baseline backbones.

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