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We investigated the training of a shared model for both text-to-speech (TTS)and voice conversion (VC) tasks. We propose using an extended modelarchitecture of Tacotron, that is a multi-source sequence-to-sequence modelwith a dual attention mechanism as the shared model for both the TTS and VCtasks. This model can accomplish these two different tasks respectivelyaccording to the type of input. An end-to-end speech synthesis task isconducted when the model is given text as the input while asequence-to-sequence voice conversion task is conducted when it is given thespeech of a source speaker as the input. Waveform signals are generated byusing WaveNet, which is conditioned by using a predicted mel-spectrogram. Wepropose jointly training a shared model as a decoder for a target speaker thatsupports multiple sources. Listening experiments show that our proposedmulti-source encoder-decoder model can efficiently achieve both the TTS and VCtasks.

Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet