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Recent works of utilizing phonetic posteriograms (PPGs) for non-parallelvoice conversion have significantly increased the usability of voice conversionsince the source and target DBs are no longer required for matching contents.In this approach, the PPGs are used as the linguistic bridge between source andtarget speaker features. However, this PPG-based non-parallel voice conversionhas some limitation that it needs two cascading networks at conversion time,making it less suitable for real-time applications and vulnerable to sourcespeaker intelligibility at conversion stage. To address this limitation, wepropose a new non-parallel voice conversion technique that employs a singleneural network for direct source-to-target voice parameter mapping. With thissingle network structure, the proposed approach can reduce both conversion timeand number of network parameters, which can be especially important factors inembedded or real-time environments. Additionally, it improves the quality ofvoice conversion by skipping the phone recognizer at conversion stage. It caneffectively prevent possible loss of phonetic information the PPG-basedindirect method suffers. Experiments show that our approach reduces number ofnetwork parameters and conversion time by 41.9% and 44.5%, respectively, withimproved voice similarity over the original PPG-based method.

Non-parallel voice conversion based on source-to-target direct mapping