Text-to-speech (TTS) systems are modelled as mel-synthesizers followed by
speech-vocoders since the era of statistical TTS that is carried forward into
neural designs. We propose an alternative approach to TTS modelling referred to
as ParrotTTS borrowing from self-supervised learning (SSL) methods. ParrotTTS
takes a two-step approach by initially training a speech-to-speech model on
unlabelled data that is abundantly available, followed by a text-to-embedding
model that leverages speech with aligned transcriptions to extend it to TTS.
ParrotTTS achieves competitive mean opinion scores on naturalness compared to
traditional TTS models but significantly improves over the latter's data
efficiency of transcribed pairs and speaker adaptation without transcriptions.
This further paves the path to training TTS models on generically trained SSL
speech models.

提出一种名为 ParrotTTS 的文本转语音建模的替代方法，该方法基于自监督学习方法。ParrotTTS 采用两步方法，在无标签数据上训练语音到语音模型，然后使用文本到嵌入模型将其扩展到 TTS。该方法在自然度方面取得了有竞争力的平均意见分数，但在转录对和讲话者自适应方面明显优于传统 TTS 模型，并进一步为使用通用训练的自监督学习语音模型训练 TTS 模型铺平了道路。