Over the past decade, a series of unflagging efforts have been dedicated to
developing highly expressive and controllable text-to-speech (TTS) systems. In
general, the holistic TTS comprises two interconnected components: the frontend
module and the backend module. The frontend excels in capturing linguistic
representations from the raw text input, while the backend module converts
linguistic cues to speech. The research community has shown growing interest in
the study of the frontend component, recognizing its pivotal role in
text-to-speech systems, including Text Normalization (TN), Prosody Boundary
Prediction (PBP), and Polyphone Disambiguation (PD). Nonetheless, the
limitations posed by insufficient annotated textual data and the reliance on
homogeneous text signals significantly undermine the effectiveness of its
supervised learning. To evade this obstacle, a novel two-stage TTS frontend
prediction pipeline, named TAP-FM, is proposed in this paper. Specifically,
during the first learning phase, we present a Multi-scale Contrastive
Text-audio Pre-training protocol (MC-TAP), which hammers at acquiring richer
insights via multi-granularity contrastive pre-training in an unsupervised
manner. Instead of mining homogeneous features in prior pre-training
approaches, our framework demonstrates the ability to delve deep into both
global and local text-audio semantic and acoustic representations. Furthermore,
a parallelized TTS frontend model is delicately devised to execute TN, PD, and
PBP prediction tasks, respectively in the second stage. Finally, extensive
experiments illustrate the superiority of our proposed method, achieving
state-of-the-art performance.

在本文中，我们提出了一种名为 TAP-FM 的新型 TTS 前端预测流水线，它包括 Multi-scale Contrastive Text-audio Pre-training（MC-TAP）协议和并行化的 TTS 前端模型，通过在无监督学习中采用多粒度对比预训练来获得更丰富的见解，以及执行 TN、PD 和 PBP 预测任务，实验证明了我们提出方法的卓越性能。