NU Voice Conversion System for the Voice Conversion Challenge 2018

Abstract

This paper presents the NU (Nagoya University) voice conversion (VC) system for the HUB task of the Voice Conversion Challenge 2018 (VCC 2018). The design of the NU VC system can basically be separated into two modules consisting of a speech parameter conversion module and a waveformprocessing module. In the speech parameter conversion module, a deep learning framework is deployed to estimate the spectral parameters of a target speaker given those of a source speaker. Specifically, a deep neural network (DNN) and a deep mixture density network (DMDN) are used as the deep model structure. In the waveform-processing module, given the estimated spectral parameters and linearly transformed F0 parameters, the converted waveform is generated using a WaveNet-based vocoder system. To use the WaveNet-based vocoder, there are several generation flows based on an analysissynthesis framework to obtain the speech parameter set, on the basis of which a system selection process is performed to select the best one in an utterance-wise manner. The results of VCC 2018 ranked the NU VC system in second place with an overall mean opinion score (MOS) of 3.44 for speech quality and 85% accuracy for speaker similarity.

An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

“…It establishes the evaluation methodology and protocol for performance benchmarking, that are adopted widely in the community. VCC 2018 [70]- [72] proposes a non-parallel…”

An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning

“…It establishes the evaluation methodology and protocol for performance benchmarking, that are adopted widely in the community. VCC 2018 [68]- [70] proposes a non-parallel training data challenge, and also connects voice conversion with anti-spoofing of speaker verification studies. VCC 2020 puts forward a cross-lingual voice conversion challenge for…”