Speaker adaptive model based on Boltzmann machine for non-parallel training in voice conversion

In this paper, we present a voice conversion (VC) method that does not use any parallel data while training the model. VC is a technique where only speaker specific information in source speech is converted while keeping the phonological information unchanged. Most of the existing VC methods rely on parallel data-pairs of speech data from the source and target speakers uttering the same sentences. However, the use of parallel data in training causes several problems; 1) the data used for the training is limited to the pre-defined sentences, 2) the trained model is only applied to the speaker pair used in the training, and 3) mismatch in alignment may happen. Although it is, thus, fairy preferable in VC not to use parallel data, a non-parallel approach is considered difficult to learn. In our approach, we realize the non-parallel training based on speaker-adaptive training (SAT). Speech signals are represented using a probabilistic model based on the Boltzmann machine that defines phonological information and speaker-related information explicitly. Speaker-independent (SI) and speaker-dependent (SD) parameters are simultaneously trained using SAT. In conversion stage, a given speech signal is decomposed into phonological and speaker-related information, the speaker-related information is replaced with that of the desired speaker, and then a voice-converted speech is obtained by mixing the two. Our experimental results showed that our approach unfortunately fell short of the popular conventional GMM-based method that used parallel data, but outperformed the conventional non-parallel approach.