Speaker-adaptive-trainable Boltzmann machine and its application to non-parallel voice conversion

In this paper, we present a voice conversion (VC) method that does not use any parallel data while training the model. Voice conversion is a technique where only speaker-specific information in the 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) a mismatch in alignment may occur. Although it is generally preferable in VC to not 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 the 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 voice-converted speech is obtained by combining the two. Our experimental results showed that our approach outperformed the conventional non-parallel approach regarding objective and subjective criteria.