Parallel Voice Conversion Based on a Continuous Sinusoidal Model

The main challenge introduced in current voice conversion is the tradeoff between speaker similarity and computational complexity. To tackle the latter problems, this paper introduces a novel sinusoidal model applied for voice conversion (VC) with parallel training data. The conventional source-filter based techniques usually give sound quality and similarity degradation of the converted voice due to parameterization errors and over smoothing, which leads to a mismatch in the converted characteristics. Therefore, we developed a VC method using continuous sinusoidal model (CSM), which decomposes the source voice into harmonic components to improve VC performance. In contrast to current VC approaches, our method is motivated by two observations. Firstly, it allows continuous fundamental frequency (F0) to avoid alignment errors that may happen in voiced and unvoiced segments and can degrade the converted speech, that is important to maintain a high converted speech quality. We secondly compare our model with two high-quality modern (MagPhase and WORLD) vocoders applied for VC, and one with a vocoder-free VC framework based on a differential Gaussian mixture model that was used recently for the Voice Conversion Challenge 2018. Similarity and intelligibility are finally evaluated in objective and subjective measures. Experimental results confirmed that the proposed method obtained higher speaker similarity compared to the conventional methods