Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis

9 Mar 2020Ting-Yao HuAshish ShrivastavaOncel TuzelChandra Dhir

We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, such as speaker information, is required. Existing unsupervised methods, during training, generate speech by computing style from the corresponding ground truth sample and use a decoder to combine the style vector with the input text... (read more)

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