Noise-Robust Voice Conversion Using High-Quefrency Boosting via Sub-Band Cepstrum Conversion and Fusion  
[PERSON]  
1 Lab of Intelligent Information Processing, Army Engineering University, Nanjing 210007, China; [EMAIL_ADDRESS] (X.M.); [EMAIL_ADDRESS] (Y.  
the source and target speakers speak the same texts. Many existing approaches that have yielded conversion results with both high quality and high similarity are based on parallel data, such as Gaussian mixture models (GMM) [2; 11], frequency warping (FW) [12; 13; 14], deep neural networks (DNN) [15; 16; 17], non-negative matrix factorization (NMF) [18; 19], and so on. This paper also focuses on VC with parallel training data.  
In the Voice Conversion Challenge 2018 (VCC 2018) [20], the methods based on bidirectional long short-term memory (BLSTM) and GMM achieved excellent results, especially the strong baseline based on GMM, which ranked second among all the submitted algorithms. In this competition, the system _N10_ submitted by the University of Science and Technology of China (USTC) and iFlytek Research achieved the best results for the parallel HUB sub-tasks. The HUB task was a sub-task of VCC 2018, which was designed to evaluate the voice conversion algorithms on a parallel corpus [20]. The system was trained on a large amount of data that consisted of hundreds of hours of noisy speech and dozens of hours of clean English speech. The trend reflected in VCC 2018 indicated the future directions of the research on VC, such as alleviating the reliance on a large amount of training data, improving the similarity between converted voices and target voices, considering the noise resistance of the conversion system, and making the system real-time. Speech recorded in daily life is more or less polluted by various kinds of noises, so it is difficult to get clean speech directly. Hence, the conversion of noisy speech plays an important role when putting VC into practical usage.  
In view of the problems mentioned above, in order to ensure the high similarity between the converted voice and the target voice, and to improve the noise robustness of the system, especially with limited training data, in this study, we investigated a noise-robust VC using high-quefrency boosting via sub-band cepstrum conversion and fusion with a BLSTM neural network. First, two different filtering methods were introduced to suppress noises in the preprocessing stage and the post-processing stage, respectively. One was the low-pass filtering of time-domain waveforms to remove part of the high frequency noises from recording, and the other was statistical filtering of Mel-cepstral coefficients (MCEPs) to reduce the interference of noises on the converted coefficients. Second, in the process of feature extraction, the feature dimension of MCEPs was extended, and two different BLSTMs were used to convert the sub-band cepstrum. The motivation for doing this was that, based on the experience of our previous experiments, when the voice is contaminated by noises, the high-quefrency components of speech seem to be vulnerable to interference. Meanwhile, in order to alleviate the problem of parameter discontinuity when concatenating a converted sub-band cepstrum, a weighted fusion scheme was proposed. Finally, the converted waveform was generated using a vocoder after statistical filtering. Experiments and subjective evaluations demonstrated that the converted voices of the proposed method held clearer pronunciations and better speaker similarity than those from the BLSTM and GMM baselines on both English and Mandarin datasets. Experiments also demonstrated that the proposed method maintained a good performance for noisy input speech.  
The remainder of this paper is organized as follows: Section 2 mainly presents related work and recent progress. Section 3 describes the proposed approach. The setup of experiments and analysis of results are discussed in Section 4. Finally, the conclusions and future work are presented in Section 5.