Voice Conversion Challenge 2018

Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. The Voice Conversion Challenge (VCC) 2016 was launched in 2016 at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. The second edition of VCC was launched in 2018, the VCC 2018. In this second edition, three aspects of the challenge were revised. First, the amount of speech data used for the construction of participant's VC systems was reduced to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition was introduced, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. Both parallel and non-parallel voice conversion systems are evaluated via the same large-scale crowdsourcing listening test. Third, bridging the gap between the ASV and VC communities was also attempted. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, spoofing performance of the VC systems based on anti-spoofing scores was assessed.

Description from: https://datashare.ed.ac.uk/handle/10283/3061