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We propose Guided-TTS, a high-quality text-to-speech (TTS) model that doesnot require any transcript of target speaker using classifier guidance.Guided-TTS combines an unconditional diffusion probabilistic model with aseparately trained phoneme classifier for classifier guidance. Ourunconditional diffusion model learns to generate speech without any contextfrom untranscribed speech data. For TTS synthesis, we guide the generativeprocess of the diffusion model with a phoneme classifier trained on alarge-scale speech recognition dataset. We present a norm-based scaling methodthat reduces the pronunciation errors of classifier guidance in Guided-TTS. Weshow that Guided-TTS achieves a performance comparable to that of thestate-of-the-art TTS model, Grad-TTS, without any transcript for LJSpeech. Wefurther demonstrate that Guided-TTS performs well on diverse datasets includinga long-form untranscribed dataset.

Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance