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The end-to-end speech synthesis model can directly take an utterance asreference audio, and generate speech from the text with prosody and speakercharacteristics similar to the reference audio. However, an appropriateacoustic embedding must be manually selected during inference. Due to the factthat only the matched text and speech are used in the training process, usingunmatched text and speech for inference would cause the model to synthesizespeech with low content quality. In this study, we propose to mitigate thesetwo problems by using multiple reference audios and style embedding constraintsrather than using only the target audio. Multiple reference audios areautomatically selected using the sentence similarity determined byBidirectional Encoder Representations from Transformers (BERT). In addition, weuse ''target'' style embedding from a Pre-trained encoder as a constraint byconsidering the mutual information between the predicted and ''target'' styleembedding. The experimental results show that the proposed model can improvethe speech naturalness and content quality with multiple reference audios andcan also outperform the baseline model in ABX preference tests of stylesimilarity.

Using multiple reference audios and style embedding constraints for speech synthesis