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.

本文介绍了一种名为 Guided-TTS 的高质量文本到语音 (TTS) 模型，使用分类器指导，不需要目标说话者的任何转录。具体来说，无条件扩散模型通过大规模语音识别数据集上训练的音素分类器指导生成 Diffusion 网络参数，实现了单批次推理下的 TTS 合成。实验结果表明，与最先进的 TTS 模型 Grad-TTS 相比，在 LJSpeech 数据集上，可以在没有转录的情况下达到性能可比的水平。同时，该模型能较好地适应多样化的数据集。

Guided-TTS: 基于分类器指导的文本语音合成扩散模型

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

We consider the problem of training speech recognition systems without using
any labeled data, under the assumption that the learner can only access to the
input utterances and a phoneme language model estimated from a non-overlapping
corpus. We propose a fully unsupervised learning algorithm that alternates
between solving two sub-problems: (i) learn a phoneme classifier for a given
set of phoneme segmentation boundaries, and (ii) refining the phoneme
boundaries based on a given classifier. To solve the first sub-problem, we
introduce a novel unsupervised cost function named Segmental Empirical Output
Distribution Matching, which generalizes the work in (Liu et al., 2017) to
segmental structures. For the second sub-problem, we develop an approximate MAP
approach to refining the boundaries obtained from Wang et al. (2017).
Experimental results on TIMIT dataset demonstrate the success of this fully
unsupervised phoneme recognition system, which achieves a phone error rate
(PER) of 41.6%. Although it is still far away from the state-of-the-art
supervised systems, we show that with oracle boundaries and matching language
model, the PER could be improved to 32.5%.This performance approaches the
supervised system of the same model architecture, demonstrating the great
potential of the proposed method.

本研究提出了一种完全无监督的学习算法，可在不使用标记数据的情况下训练语音识别系统，其关键在于使用音位语言模型和输入话语。该算法在解决两个子问题 (即学习给定音位划分边界下的音位分类器和更新音位边界) 上交替进行，并在 TIMIT 数据集上获得了令人满意的性能提升。