Statistical voice conversion (VC) is a technique to convert specific non-or paralinguistic information while keeping linguistic information unchanged, and speaker conversion has been studied as a typical application of VC for a few decades. To better understand various VC techniques using a freely available common dataset, the Voice Conversion Challenge (VCC) was launched in 2016 and the 2nd challenge was held in 2018. As one of the baseline systems for VCC 2018, we developed open-source VC software called "sprocket", in which not only conventional techniques, such as a trajectory-based conversion method using a Gaussian mixture model (GMM) and a vocoderbased conversion framework but also recently developed techniques, such as a vocoder-free VC framework, have been implemented. Using sprocket, it is possible to 1) easily reproduce converted voices using the VCC datasets and 2) develop VC systems using other parallel speech datasets with fundamental VC functions, such as acoustic feature extraction, time alignment between the source and target features, GMM training, feature conversion, and waveform generation. In this paper, we describe 1) the technical details and use of sprocket, 2) the development of the baseline systems for the HUB and SPOKE tasks of VCC 2018 using sprocket, and 3) the performance of sprocket as a VC system by demonstrating results for our developed baseline systems in VCC 2018.

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