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Voice conversion (VC) is a task to transform a person's voice to differentstyle while conserving linguistic contents. Previous state-of-the-art on VC isbased on sequence-to-sequence (seq2seq) model, which could mislead linguisticinformation. There was an attempt to overcome it by using textual supervision,it requires explicit alignment which loses the benefit of using seq2seq model.In this paper, a voice converter using multitask learning with text-to-speech(TTS) is presented. The embedding space of seq2seq-based TTS has abundantinformation on the text. The role of the decoder of TTS is to convert embeddingspace to speech, which is same to VC. In the proposed model, the whole networkis trained to minimize loss of VC and TTS. VC is expected to capture morelinguistic information and to preserve training stability by multitasklearning. Experiments of VC were performed on a male Korean emotionaltext-speech dataset, and it is shown that multitask learning is helpful to keeplinguistic contents in VC.

Emotional Voice Conversion using Multitask Learning with Text-to-speech