# A unified front-end framework for English text-to-speech synthesis

arXiv: https://arxiv.org/abs/2305.10666 · HTML: https://mlsanity.com/paper/2305.10666/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [End-to-End Automatic Speech Translation of Audiobooks](https://mlsanity.com/paper/1802.04200/) (2018)
- [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://mlsanity.com/paper/2009.11136/) (2020)
- [Transformer based Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/2004.06338/) (2020)
- [RNN Approaches to Text Normalization: A Challenge](https://mlsanity.com/paper/1611.00068/) (2016)
- [Unified Mandarin TTS Front-end Based on Distilled BERT Model](https://mlsanity.com/paper/2012.15404/) (2020)
- [Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models](https://mlsanity.com/paper/1610.06540/) (2016)
- [A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis](https://mlsanity.com/paper/1911.04111/) (2019)
- [r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation](https://mlsanity.com/paper/2202.11194/) (2022)
- [Masked Conditional Random Fields for Sequence Labeling](https://mlsanity.com/paper/2103.10682/) (2021)
- [A hybrid text normalization system using multi-head self-attention for mandarin](https://mlsanity.com/paper/1911.04128/) (2019)
- [Transformer-based Models of Text Normalization for Speech Applications](https://mlsanity.com/paper/2202.00153/) (2022)

## Built on by (most cited first, 4 of 4)

- [Multi-Task Learning for Front-End Text Processing in TTS](https://mlsanity.com/paper/2401.06321/) (2024)
- [Acquiring Pronunciation Knowledge from Transcribed Speech Audio via Multi-task Learning](https://mlsanity.com/paper/2409.09891/) (2024)
- [Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation](https://mlsanity.com/paper/2506.07646/) (2025)
- [Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation](https://mlsanity.com/paper/2605.02496/) (2026)

## Abstract (arXiv)

The front-end is a critical component of English text-to-speech (TTS) systems, responsible for extracting linguistic features that are essential for a text-to-speech model to synthesize speech, such as prosodies and phonemes. The English TTS front-end typically consists of a text normalization (TN) module, a prosody word prosody phrase (PWPP) module, and a grapheme-to-phoneme (G2P) module. However, current research on the English TTS front-end focuses solely on individual modules, neglecting the interdependence between them and resulting in sub-optimal performance for each module. Therefore, this paper proposes a unified front-end framework that captures the dependencies among the English TTS front-end modules. Extensive experiments have demonstrated that the proposed method achieves state-of-the-art (SOTA) performance in all modules.