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Text-to-speech (TTS) and voice conversion (VC) are two different tasks bothaiming at generating high quality speaking voice according to different inputmodality. Due to their similarity, this paper proposes UnifySpeech, whichbrings TTS and VC into a unified framework for the first time. The model isbased on the assumption that speech can be decoupled into three independentcomponents: content information, speaker information, prosody information. BothTTS and VC can be regarded as mining these three parts of information from theinput and completing the reconstruction of speech. For TTS, the speech contentinformation is derived from the text, while in VC it's derived from the sourcespeech, so all the remaining units are shared except for the speech contentextraction module in the two tasks. We applied vector quantization and domainconstrain to bridge the gap between the content domains of TTS and VC.Objective and subjective evaluation shows that by combining the two task, TTSobtains better speaker modeling ability while VC gets hold of impressive speechcontent decoupling capability.

UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion