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Unsupervised speaker and expression factorization for multi-speaker expressive synthesis of ebooks

This work aims to improve expressive speech synthesis of ebooks for multiple speakers by using training data from many audiobooks. Audiobooks contain a wide variety of expressive speaking styles which are often impractical to annotate. However, the speaker-expression factorization (SEF) framework, which has been proven to be a powerful tool in speaker and expression modelling usually requires the (supervised) information about expressions in the training data. This work presents an unsupervised SEF method which implements the SEF on unlabelled training data in the framework of cluster adaptive training (CAT). The proposed method integrates the expression clustering and parameter estimation in a single process to maximize the likelihood of the training data. Experimental results indicate that it outperforms the cascade system of expression clustering and supervised SEF, and significantly improves the expressiveness of the synthetic speech of different speakers.