## 4 Experiments and Results  
### Experimental Setup and Implementation Details  
The experiments were conducted on an English dataset CMU ARCTIC [30] and a Mandarin dataset THCHS-30 [31]. Babble noises were added to the utterances of all the source speakers with 15 dB signal-to-noise ratio (SNR) to evaluate the noise robustness of the VC methods. The voice signals of CMU ARCTIC were resampled to 16 kHz with a mono channel. The window length was 25 m and the frame shift was 5 m. Four speakers were chosen: two male speakers, bdl and jmk, and two female speakers, slt and clb. Both intra-gender and inter-gender conversions were conducted between the following pairs of speakers: bdl to jmk (male to male, M-M), clb to slt (female to female, F-F), clb to jmk (female to male, F-M), and bdl to slt (male to female, M-F). The voice signals of THCHS-30 were also resampled to 16 kHz. A male speaker (A8) and two female speakers (A2 and A23) were chosen and A2 to A23 (F-F), A8 to A2 (M-F) were taken as the intra-gender and inter-gender conversions, respectively. In the experiments on both databases, 200 utterances were randomly selected for training, and another 15 utterances of each speaker were randomly chosen for evaluation, where there was no overlap between the training and the evaluation utterances.  
In our work, WORLD was used as the vocoder to analysis and synthesis voice signals. For voice conversion purposes, in order to avoid underfitting and reduce the training complexity of the data, low dimensional MCEPs were usually adopted as spectral features. However, thanks to the sub-band cepstrum processing scheme proposed in this paper, our method was able to process relatively high-dimensional MCEPs without training difficulty. The high-dimensional representation contained many details of speakers, which should be retained and utilized, especially in the cases with limited training data. In the following experiments, 129-dimensional MCEPs were calculated from the spectrum using a Speech Signal Processing Toolkit (SPTK), where the 128-dimensional MCEPs (except the energy feature. i.e., \(MC_{full}\) in Figure 3) were divided into the 64-dimensional low-quefrency MCEPs and the 64-dimensional high-quefrency sub-bands (i.e., \(mcep_{low}\) and \(mcep_{high}\) in Figure 3) for subsequent conversion models [32].  
The models for cepstrum conversion in this paper included two BLSTM networks, i.e., BLSTM1 and BLSTM2 for low- and high-quefrency bands, respectively. BLSTM1 had three hidden layers, and the number of units in each layer was [128 256 128], respectively. Given the low fluctuation of parameters of the high-quefrency bands, BLSTM2 took a relatively small units, and the number of units in each layer was [64 128 64], respectively. A dropout layer with parameter 0.5 was introduced to avoid overfitting. The learning rate was adjusted automatically from \(1.0\times 10^{-3}\) to \(1.0\times 10^{-5}\).  
Three competitive systems of VC were chosen as baselines for comparison:  
GMM: A conventional GMM-based voice conversion system, which was one of the baseline approaches in VCC 2018 [21].  
DIFGMM: A strong baseline for VCC 2018.  
BLSTM: A BLSTM with three hidden layers for voice conversion from [PERSON] et al. [15].