  We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does
not require any transcript of target speaker using classifier guidance.
Guided-TTS combines an unconditional diffusion probabilistic model with a
separately trained phoneme classifier for classifier guidance. Our
unconditional diffusion model learns to generate speech without any context
from untranscribed speech data. For TTS synthesis, we guide the generative
process of the diffusion model with a phoneme classifier trained on a
large-scale speech recognition dataset. We present a norm-based scaling method
that reduces the pronunciation errors of classifier guidance in Guided-TTS. We
show that Guided-TTS achieves a performance comparable to that of the
state-of-the-art TTS model, Grad-TTS, without any transcript for LJSpeech. We
further demonstrate that Guided-TTS performs well on diverse datasets including
a long-form untranscribed dataset.
