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The task of few-shot style transfer for voice cloning in text-to-speech (TTS)synthesis aims at transferring speaking styles of an arbitrary source speakerto a target speaker's voice using very limited amount of neutral data. This isa very challenging task since the learning algorithm needs to deal withfew-shot voice cloning and speaker-prosody disentanglement at the same time.Accelerating the adaptation process for a new target speaker is of importancein real-world applications, but even more challenging. In this paper, weapproach to the hard fast few-shot style transfer for voice cloning task usingmeta learning. We investigate the model-agnostic meta-learning (MAML) algorithmand meta-transfer a pre-trained multi-speaker and multi-prosody base TTS modelto be highly sensitive for adaptation with few samples. Domain adversarialtraining mechanism and orthogonal constraint are adopted to disentangle speakerand prosody representations for effective cross-speaker style transfer.Experimental results show that the proposed approach is able to conduct fastvoice cloning using only 5 samples (around 12 second speech data) from a targetspeaker, with only 100 adaptation steps. Audio samples are available online.

Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning