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arxiv: 1804.04262 · v1 · pith:2INIAEHQnew · submitted 2018-04-12 · 📡 eess.AS · cs.CL· cs.SD· stat.ML

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

classification 📡 eess.AS cs.CLcs.SDstat.ML
keywords challengeconversionspeakervoiceidentityparallelspokestate-of-the-art
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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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  1. Non-Parallel Voice Conversion with Cyclic Variational Autoencoder

    eess.AS 2019-07 unverdicted novelty 6.0

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