The USTC System for Voice Conversion Challenge 2016: Neural Network Based Approaches for Spectrum, Aperiodicity and F0 Conversion

This paper introduces the methods we adopt to build our system for the evaluation event of Voice Conversion Challenge (VCC) 2016. We propose to use neural network-based approaches to convert both spectral and excitation features. First, the generatively trained deep neural network (GTDNN) is adopted for spectral envelope conversion after the spectral envelopes have been pre-processed by frequency warping. Second, we propose to use a recurrent neural network (RNN) with long short-term memory (LSTM) cells for F0 trajectory conversion. In addition, we adopt a DNN for band aperiodicity conversion. Both internal tests and formal VCC evaluation results demonstrate the effectiveness of the proposed methods.