Voice Conversion with SI-DNN and KL Divergence Based Mapping without Parallel Training Data

Publication date: Available online 30 November 2018Source: Speech CommunicationAuthor(s): Feng-Long Xie, Frank K. Soong, Haifeng LiAbstractWe propose a Speaker Independent Deep Neural Net (SI-DNN) and Kullback- Leibler Divergence (KLD) based mapping approach to voice conversion without using parallel training data. The acoustic difference between source and target speakers is equalized with SI-DNN via its estimated output posteriors, which serve as a probabilistic mapping from acoustic input frames to the corresponding symbols in the phonetic space. KLD is chosen as an ideal distortion measure to find an appropriate mapping from each input source speaker's frame to that of the target speaker. The mapped acoustic segments of the target speaker form the construction bases for voice convers...

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