Prior-agnostic Multi-scale Contrastive Text-Audio Pre-training for Parallelized TTS Frontend Modeling

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.